Practical Text Mining And Statistical Analysis For Non Structured Text Data Applications

Practical Text Mining And Statistical Analysis For Non Structured Text Data Applications Book PDF
✏Book Title : Practical Text Mining and Statistical Analysis for Non structured Text Data Applications
✏Author : Gary Miner
✏Publisher : Academic Press
✏Release Date : 2012
✏Pages : 1053
✏ISBN : 9780123869791
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Practical Text Mining and Statistical Analysis for Non structured Text Data Applications Book Summary : The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. -Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible -Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com -Glossary of text mining terms provided in the appendix

Handbook Of Statistical Analysis And Data Mining Applications Book PDF
✏Book Title : Handbook of Statistical Analysis and Data Mining Applications
✏Author : Robert Nisbet
✏Publisher : Elsevier
✏Release Date : 2017-11-09
✏Pages : 822
✏ISBN : 9780124166455
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Handbook of Statistical Analysis and Data Mining Applications Book Summary : Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

📒Text Mining And Analysis ✍ Dr. Goutam Chakraborty

Text Mining And Analysis Book PDF
✏Book Title : Text Mining and Analysis
✏Author : Dr. Goutam Chakraborty
✏Publisher : SAS Institute
✏Release Date : 2014-11-22
✏Pages : 340
✏ISBN : 9781612907871
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Text Mining and Analysis Book Summary : Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis. This book is part of the SAS Press program.

Practical Predictive Analytics And Decisioning Systems For Medicine Book PDF
✏Book Title : Practical Predictive Analytics and Decisioning Systems for Medicine
✏Author : Linda Miner
✏Publisher : Academic Press
✏Release Date : 2014-09-27
✏Pages : 1110
✏ISBN : 9780124116405
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Practical Predictive Analytics and Decisioning Systems for Medicine Book Summary : With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner. Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations Demonstrates methods to help sort through data to make better observations and allow you to make better predictions

8th International Conference On Practical Applications Of Computational Biology Bioinformatics Pacbb 2014  Book PDF
✏Book Title : 8th International Conference on Practical Applications of Computational Biology Bioinformatics PACBB 2014
✏Author : Julio Saez-Rodriguez
✏Publisher : Springer
✏Release Date : 2014-05-21
✏Pages : 294
✏ISBN : 9783319075815
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏8th International Conference on Practical Applications of Computational Biology Bioinformatics PACBB 2014 Book Summary : Biological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. The impressive capabilities of next generation sequencing technologies, together with novel and ever evolving distinct types of omics data technologies, have put an increasingly complex set of challenges for the growing fields of Bioinformatics and Computational Biology. The analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as Databases, Statistics, Data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Clearly, Biology is more and more a science of information requiring tools from the computational sciences. In the last few years, we have seen the surge of a new generation of interdisciplinary scientists that have a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance boosting the research efforts in the field and contributing to the education of a new generation of Bioinformatics scientists. PACBB‘14 contributes to this effort promoting this fruitful interaction. PACBB'14 technical program included 34 papers spanning many different sub-fields in Bioinformatics and Computational Biology. Therefore, the conference promotes the interaction of scientists from diverse research groups and with a distinct background such as computer scientists, mathematicians or biologists.

Computational Intelligence Applications In Business Intelligence And Big Data Analytics Book PDF
✏Book Title : Computational Intelligence Applications in Business Intelligence and Big Data Analytics
✏Author : Vijayan Sugumaran
✏Publisher : CRC Press
✏Release Date : 2017-06-26
✏Pages : 362
✏ISBN : 9781351720243
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Computational Intelligence Applications in Business Intelligence and Big Data Analytics Book Summary : There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

Text Mining With Machine Learning Book PDF
✏Book Title : Text Mining with Machine Learning
✏Author : Jan Žižka
✏Publisher : CRC Press
✏Release Date : 2019-10-31
✏Pages : 352
✏ISBN : 9780429890277
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Text Mining with Machine Learning Book Summary : This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.

📒Practical Text Analytics ✍ Steven Struhl

Practical Text Analytics Book PDF
✏Book Title : Practical Text Analytics
✏Author : Steven Struhl
✏Publisher : Kogan Page Publishers
✏Release Date : 2015-07-03
✏Pages : 272
✏ISBN : 9780749474027
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Practical Text Analytics Book Summary : In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Practical Text Analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics. The book presents the analysis process so that it is immediately understood by the marketing professionals who must use it, so they can apply proven concepts and methods correctly and with confidence. By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data. Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.

📒Real World Data Mining ✍ Dursun Delen

Real World Data Mining Book PDF
✏Book Title : Real World Data Mining
✏Author : Dursun Delen
✏Publisher : FT Press
✏Release Date : 2014-12-16
✏Pages : 288
✏ISBN : 9780133551112
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Real World Data Mining Book Summary : Use the latest data mining best practices to enable timely, actionable, evidence-based decision making throughout your organization! Real-World Data Mining demystifies current best practices, showing how to use data mining to uncover hidden patterns and correlations, and leverage these to improve all aspects of business performance. Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, he provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: processes, methods, techniques, tools, and metrics; the role and management of data; text and web mining; sentiment analysis; and Big Data integration. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials. Real-World Data Mining will be valuable to professionals on analytics teams; professionals seeking certification in the field; and undergraduate or graduate students in any analytics program: concentrations, certificate-based, or degree-based.

Healthcare S Out Sick Predicting A Cure Solutions That Work  Book PDF
✏Book Title : HEALTHCARE s OUT SICK PREDICTING A CURE Solutions that WORK
✏Author : Gary D. Miner
✏Publisher : CRC Press
✏Release Date : 2019-01-04
✏Pages : 280
✏ISBN : 9780429015816
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏HEALTHCARE s OUT SICK PREDICTING A CURE Solutions that WORK Book Summary : The U.S. healthcare system is in "complete chaos-disarray." Medical costs have increased significantly over the past 6 years with 70% increase for deductibles and 24% or more for health insurance premiums. All the while, workers earnings have either not increased or if they did, the pay raises were for less than the increase in the cost of medical care. The situation is unsustainable and the public wants the system fixed. This book offers ways of fixing the problems in healthcare. HEALTHCARE's OUT SICK - PREDICTING A CURE - Solutions that WORK !!!! first defines the "healthcare in crisis" problem. Through real patient experiences, the book describes the difficulties of getting through the maze of complexity among the plethora of "silo providers" which make up the industry. The heart of the book provides readers with a comprehensive solution that can work, a disruption that is necessary to provide Americans the medical care they need without the US public and healthcare providers and payors going into bankruptcy, insolvency or closure. This book delves into digitized medicine, payor and provider reimbursement models, and value-based healthcare delivery. It also includes a philosophy or mode of thinking and operation for the solutions that are needed for diagnosis-effective, cost-effective, and time-efficient healthcare delivery, of which digitized medicine, value-based care, and payor reimbursement modes are just some of the factors. The authors propose that the real solution involves having the patient at the center of the issues and changing from an archaic gold standard way of thinking to a "Predictive Analytic thinking" where one gets at the real truth by doing "real science" that in the end becomes effective not only for the population but for the individual person. This all leads to real person-centered and person-directed medicine and healthcare delivery.

Fundamentals Of Predictive Text Mining Book PDF
✏Book Title : Fundamentals of Predictive Text Mining
✏Author : Sholom M. Weiss
✏Publisher : Springer
✏Release Date : 2015-09-07
✏Pages : 239
✏ISBN : 9781447167501
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Fundamentals of Predictive Text Mining Book Summary : This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.

📒Text Mining And Analysis ✍ Goutam Chakraborty

Text Mining And Analysis Book PDF
✏Book Title : Text Mining and Analysis
✏Author : Goutam Chakraborty
✏Publisher : SAS Institute
✏Release Date : 2013-10
✏Pages : 320
✏ISBN : 9781612905518
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Text Mining and Analysis Book Summary : Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis.

Mastering Text Mining With R Book PDF
✏Book Title : Mastering Text Mining with R
✏Author : Ashish Kumar
✏Publisher : Packt Publishing Ltd
✏Release Date : 2016-12-28
✏Pages : 258
✏ISBN : 9781782174707
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Mastering Text Mining with R Book Summary : Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful. What You Will Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) Build a baseline sentence completing application Perform entity extraction and named entity recognition using R In Detail Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media. Style and approach This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.

Emerging Technologies Of Text Mining Techniques And Applications Book PDF
✏Book Title : Emerging Technologies of Text Mining Techniques and Applications
✏Author : do Prado, Hercules Antonio
✏Publisher : IGI Global
✏Release Date : 2007-10-31
✏Pages : 376
✏ISBN : 9781599043753
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Emerging Technologies of Text Mining Techniques and Applications Book Summary : "This book provides the most recent technical information related to the computational models of the text mining process, discussing techniques within the realms of classification, association analysis, information extraction, and clustering. Offering an innovative approach to the utilization of textual information mining to maximize competitive advantage, it will provide libraries with the defining reference on this topic"--Provided by publisher.

📒Text Mining ✍ Sholom M. Weiss

Text Mining Book PDF
✏Book Title : Text Mining
✏Author : Sholom M. Weiss
✏Publisher : Springer Science & Business Media
✏Release Date : 2010-01-08
✏Pages : 237
✏ISBN : 0387345558
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Text Mining Book Summary : Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

Text Mining Application Programming Book PDF
✏Book Title : Text Mining Application Programming
✏Author : Manu Konchady
✏Publisher :
✏Release Date : 2006
✏Pages : 412
✏ISBN : UOM:39015064690780
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Text Mining Application Programming Book Summary : Text Mining Application Programming teaches software developers how to mine the vast amounts of information available on the Web, internal networks, and desktop files and turn it into usable data. The book helps developers understand the problems associated with managing unstructured text, and explains how to build your own mining tools using standard statistical methods from information theory, artificial intelligence, and operations research. Each of the topics covered are thoroughly explained and then a practical implementation is provided.The book begins with a brief overview of text data, where it can be found, and the typical search engines and tools used to search and gather this text. It details how to build tools for extracting and using the text, and covers the mathematics behind many of the algorithms used in building these tools. From there you'll learn how to build tokens from text, construct indexes, and detect patterns in text. You'll also find methods to extract the names of people, places, and organizations from an email, a news article, or a Web page. The next portion of the book teaches you how to find information on the Web, the structure of the Web, and how to build spiders to crawl the Web. Text categorization is also described in the context of managing email. The final part of the book covers information monitoring, summarization, and a simple Question & Answer (Q&A) system. The code used in the book is written in Perl, but knowledge of Perl is not necessary to run the software. Developers with an intermediate level of experience with Perl can customize the software. Although the book is about programming, methods are explained with English-like pseudocode and the source code is provided on the CD-ROM. After reading this book, you'll be ready to tap into the bevy of information available online in ways you never thought possible.

Data Mining Practical Machine Learning Tools And Techniques Book PDF
✏Book Title : Data Mining Practical Machine Learning Tools and Techniques
✏Author : Ian H. Witten
✏Publisher : Elsevier
✏Release Date : 2011-02-03
✏Pages : 664
✏ISBN : 9780080890364
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Data Mining Practical Machine Learning Tools and Techniques Book Summary : Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Practical Text Mining With Perl Book PDF
✏Book Title : Practical Text Mining with Perl
✏Author : Roger Bilisoly
✏Publisher : John Wiley & Sons Incorporated
✏Release Date : 2008-08-18
✏Pages : 295
✏ISBN : UCSC:32106019980777
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Practical Text Mining with Perl Book Summary : This text shows the reader how to actually perform text mining. It emphasises practical examples using open source tools applied to freely available data over the Internet and provides programming code and program output for practical examples of analysing text.

📒R Data Mining ✍ Andrea Cirillo

R Data Mining Book PDF
✏Book Title : R Data Mining
✏Author : Andrea Cirillo
✏Publisher : Packt Publishing Ltd
✏Release Date : 2017-11-29
✏Pages : 442
✏ISBN : 9781787129238
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏R Data Mining Book Summary : Mine valuable insights from your data using popular tools and techniques in R About This Book Understand the basics of data mining and why R is a perfect tool for it. Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it. Apply effective data mining models to perform regression and classification tasks. Who This Book Is For If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required. What You Will Learn Master relevant packages such as dplyr, ggplot2 and so on for data mining Learn how to effectively organize a data mining project through the CRISP-DM methodology Implement data cleaning and validation tasks to get your data ready for data mining activities Execute Exploratory Data Analysis both the numerical and the graphical way Develop simple and multiple regression models along with logistic regression Apply basic ensemble learning techniques to join together results from different data mining models Perform text mining analysis from unstructured pdf files and textual data Produce reports to effectively communicate objectives, methods, and insights of your analyses In Detail R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets. Style and approach This book takes a practical, step-by-step approach to explain the concepts of data mining. Practical use-cases involving real-world datasets are used throughout the book to clearly explain theoretical concepts.

📒

  Book PDF
✏Book Title :
✏Author :
✏Publisher :
✏Release Date : 2015-12
✏Pages :
✏ISBN : UCSB:31205037402656
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏ Book Summary :

Sas Text Analytics For Business Applications Book PDF
✏Book Title : SAS Text Analytics for Business Applications
✏Author : Teresa Jade
✏Publisher : SAS Institute
✏Release Date : 2019-03-29
✏Pages : 308
✏ISBN : 9781635266610
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏SAS Text Analytics for Business Applications Book Summary : Extract actionable insights from text and unstructured data. Information extraction is the task of automatically extracting structured information from unstructured or semi-structured text. SAS Text Analytics for Business Applications: Concept Rules for Information Extraction Models focuses on this key element of natural language processing (NLP) and provides real-world guidance on the effective application of text analytics. Using scenarios and data based on business cases across many different domains and industries, the book includes many helpful tips and best practices from SAS text analytics experts to ensure fast, valuable insight from your textual data. Written for a broad audience of beginning, intermediate, and advanced users of SAS text analytics products, including SAS Visual Text Analytics, SAS Contextual Analysis, and SAS Enterprise Content Categorization, this book provides a solid technical reference. You will learn the SAS information extraction toolkit, broaden your knowledge of rule-based methods, and answer new business questions. As your practical experience grows, this book will serve as a reference to deepen your expertise.

Foundations Of Statistical Inference Book PDF
✏Book Title : Foundations of Statistical Inference
✏Author : Yoel Haitovsky
✏Publisher : Springer Science & Business Media
✏Release Date : 2003-05-22
✏Pages : 230
✏ISBN : 3790800473
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Foundations of Statistical Inference Book Summary : This volume is a compressed survey containing recent results on statistics of stochastic processes and on identification with incomplete observations. It comprises a collection of papers presented at the Shoresh Conference 2000 on the Foundation of Statistical Inference. The papers cover the following areas with high research activity: - Identification with Incomplete Observations, Data Mining, - Bayesian Methods and Modelling, - Testing, Goodness of Fit and Randomness, - Statistics of Stationary Processes.

📒Unstructured Data Analytics ✍ Jean Paul Isson

Unstructured Data Analytics Book PDF
✏Book Title : Unstructured Data Analytics
✏Author : Jean Paul Isson
✏Publisher : John Wiley & Sons
✏Release Date : 2018-03-13
✏Pages : 432
✏ISBN : 9781119129752
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Unstructured Data Analytics Book Summary : Turn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis.

Current Index To Statistics Applications Methods And Theory Book PDF
✏Book Title : Current Index to Statistics Applications Methods and Theory
✏Author :
✏Publisher :
✏Release Date : 1998
✏Pages :
✏ISBN : UOM:39015053598127
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Current Index to Statistics Applications Methods and Theory Book Summary :

Modeling Techniques In Predictive Analytics Book PDF
✏Book Title : Modeling Techniques in Predictive Analytics
✏Author : Thomas W. Miller
✏Publisher : FT Press
✏Release Date : 2014-09-29
✏Pages : 384
✏ISBN : 9780133886191
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Modeling Techniques in Predictive Analytics Book Summary : To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Predictive Analysis With Sap Book PDF
✏Book Title : Predictive Analysis with SAP
✏Author : John MacGregor
✏Publisher : SAP PRESS
✏Release Date : 2013-11-01
✏Pages : 525
✏ISBN : 1592299156
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Predictive Analysis with SAP Book Summary : From your customer's purchasing habits to your employee's key performance indicators, data can provide business insight and predict future market trends. With this comprehensive guide to SAP's Predictive Analysis toolset, anticipate market changes, drive decision making and unlock the potential of your data sources. Filled with practical examples, case studies, and business explanations, this book will simplify the complex predictive analysis process and teach you how to use SAP's Predictive Analysis toolset to understand what your data has been trying to tell you. Extensive coverage of the Predictive Analysis module, the PAL Library, R-Integration, and HANA will enable you to choose the best algorithm for your needs. Learn how to organize large amounts of data into comprehensive data models, visualizations, and reports with ease!.

📒Data Analytics ✍ Vernon Barrett

Data Analytics Book PDF
✏Book Title : Data Analytics
✏Author : Vernon Barrett
✏Publisher : Createspace Independent Publishing Platform
✏Release Date : 2017-05
✏Pages : 82
✏ISBN : 154641844X
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Data Analytics Book Summary : Data Analytics - A Comprehensive Beginner's Guide The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored. The book is designed for the senior level undergraduate, and postgraduate students of computer science and engineering. Here Is A Preview Of What Inside The Book: Big data Statistics in practice Descriptive and Inferential Statistics Parameters and Statistics Statistical data analysis Variables SUMMARY OF THE GENERAL METHOD OF DECISION ANALYSIS ANOTHER DECISION TREE MODEL AND ITS ANALYSIS Making Data Work for You Predictive Modeling Techniques Take Action Today and Learn Data Analytics in no time! Click the "Buy now with 1-Click" to the right and get this guide immediately.

Modeling Techniques In Predictive Analytics With Python And R Book PDF
✏Book Title : Modeling Techniques in Predictive Analytics with Python and R
✏Author : Thomas W. Miller
✏Publisher : FT Press
✏Release Date : 2014-09-29
✏Pages : 448
✏ISBN : 9780133892147
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Modeling Techniques in Predictive Analytics with Python and R Book Summary : Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

📒Outlier Analysis ✍ Charu C. Aggarwal

Outlier Analysis Book PDF
✏Book Title : Outlier Analysis
✏Author : Charu C. Aggarwal
✏Publisher : Springer Science & Business Media
✏Release Date : 2013-01-11
✏Pages : 446
✏ISBN : 9781461463962
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Outlier Analysis Book Summary : With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions– the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.

Big Data Analytics For Entrepreneurial Success Book PDF
✏Book Title : Big Data Analytics for Entrepreneurial Success
✏Author : Sedkaoui, Soraya
✏Publisher : IGI Global
✏Release Date : 2018-11-09
✏Pages : 300
✏ISBN : 9781522576105
✏Available Language : English, Spanish, And French

Click Here To Get Book

✏Big Data Analytics for Entrepreneurial Success Book Summary : In a resolutely practical and data-driven project universe, the digital age changed the way data is collected, stored, analyzed, visualized and protected, transforming business opportunities and strategies. It is important for today’s organizations and entrepreneurs to implement a robust data strategy and industrialize a set of “data-driven” solutions to utilize big data analytics to its fullest potential. Big Data Analytics for Entrepreneurial Success provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques within business applications. Featuring coverage on a broad range of topics such as algorithms, data collection, and machine learning, this publication provides concrete examples and case studies of successful uses of data-driven projects as well as the challenges and opportunities of generating value from data using analytics. It is ideally designed for entrepreneurs, researchers, business owners, managers, graduate students, academicians, software developers, and IT professionals seeking current research on the essential tools and technologies for organizing, analyzing, and benefiting from big data.