Computational Intelligence in Data Mining

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  • Author : Giacomo Della Riccia
  • Publisher : Springer Science & Business Media
  • Pages : 166 pages
  • ISBN : 9783211833261
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Computational Intelligence in Data Mining

Download or Read online Computational Intelligence in Data Mining full in PDF, ePub and kindle. this book written by Giacomo Della Riccia and published by Springer Science & Business Media which was released on 31 May 2000 with total page 166 pages. We cannot guarantee that Computational Intelligence in Data Mining book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of current research and industrial development, Computational Intelligence, incorporates techniques like data fusion, uncertain reasoning, heuristic search, learning, and soft computing. Data Mining focuses on unscrambling unknown patterns or structures in very large data sets. Under the headline "Discovering Structures in Large Databases” the book starts with a unified view on 'Data Mining and Statistics – A System Point of View'. Two special techniques follow: 'Subgroup Mining', and 'Data Mining with Possibilistic Graphical Models'. "Data Fusion and Possibilistic or Fuzzy Data Analysis” is the next area of interest. An overview of possibilistic logic, nonmonotonic reasoning and data fusion is given, the coherence problem between data and non-linear fuzzy models is tackled, and outlier detection based on learning of fuzzy models is studied. In the domain of "Classification and Decomposition” adaptive clustering and visualisation of high dimensional data sets is introduced. Finally, in the section "Learning and Data Fusion” learning of special multi-agents of virtual soccer is considered. The last topic is on data fusion based on stochastic models.

Data Mining Techniques

Data Mining Techniques
  • Author : Michael J. A. Berry,Gordon S. Linoff
  • Publisher : John Wiley & Sons
  • Release : 09 April 2004
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Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.

Computational Intelligence in Data Mining

Computational Intelligence in Data Mining
  • Author : Giacomo Della Riccia,Rudolf Kruse,Hans-J. Lenz,Hans-Joachim Lenz
  • Publisher : Springer Science & Business Media
  • Release : 31 May 2000
GET THIS BOOK Computational Intelligence in Data Mining

The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of current research and industrial development, Computational Intelligence, incorporates techniques like data fusion, uncertain reasoning, heuristic search, learning, and soft computing. Data Mining focuses on unscrambling unknown patterns or structures in very large data sets. Under the headline "Discovering Structures in Large Databases” the book starts with a unified view on 'Data Mining and Statistics – A System Point of View'. Two special techniques follow: 'Subgroup

Data Mining

Data Mining
  • Author : Charu C. Aggarwal
  • Publisher : Springer
  • Release : 13 April 2015
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This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this

Data Mining Concepts and Techniques

Data Mining  Concepts and Techniques
  • Author : Jiawei Han,Jian Pei,Micheline Kamber
  • Publisher : Elsevier
  • Release : 09 June 2011
GET THIS BOOK Data Mining Concepts and Techniques

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing

Educational Data Mining

Educational Data Mining
  • Author : Alejandro Peña-Ayala
  • Publisher : Springer
  • Release : 08 November 2013
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This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. ·

Data Mining

Data Mining
  • Author : Jiawei Han,Micheline Kamber
  • Publisher : Morgan Kaufmann
  • Release : 30 July 2021
GET THIS BOOK Data Mining

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and

Practical Applications of Data Mining

Practical Applications of Data Mining
  • Author : Sang C. Suh
  • Publisher : Jones & Bartlett Publishers
  • Release : 30 July 2021
GET THIS BOOK Practical Applications of Data Mining

Various topics of data mining techniques are identified and described throughout, including clustering, association rules, rough set theory, probability theory, neural networks, classification, and fuzzy logic. Each of these techniques is explored with a theoretical introduction and its effectiveness is demonstrated with various chapter examples.

Data Mining

Data Mining
  • Author : Florin Gorunescu
  • Publisher : Springer
  • Release : 29 May 2013
GET THIS BOOK Data Mining

The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the ‘natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since “knowledge

Data Preprocessing in Data Mining

Data Preprocessing in Data Mining
  • Author : Salvador García,Julián Luengo,Francisco Herrera
  • Publisher : Springer
  • Release : 30 August 2014
GET THIS BOOK Data Preprocessing in Data Mining

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the

R Data Mining

R Data Mining
  • Author : Andrea Cirillo
  • Publisher : Packt Publishing Ltd
  • Release : 29 November 2017
GET THIS BOOK R Data Mining

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

Data Mining with Rattle and R

Data Mining with Rattle and R
  • Author : Graham Williams
  • Publisher : Springer
  • Release : 25 February 2011
GET THIS BOOK Data Mining with Rattle and R

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data

Learn Data Mining Through Excel

Learn Data Mining Through Excel
  • Author : Hong Zhou
  • Publisher : Apress
  • Release : 24 June 2020
GET THIS BOOK Learn Data Mining Through Excel

Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can

5th International Symposium on Data Mining Applications

5th International Symposium on Data Mining Applications
  • Author : Mamdouh Alenezi,Basit Qureshi
  • Publisher : Springer
  • Release : 28 March 2018
GET THIS BOOK 5th International Symposium on Data Mining Applications

The 5th Symposium on Data Mining Applications (SDMA 2018) provides valuable opportunities for technical collaboration among data mining and machine learning researchers in Saudi Arabia, Gulf Cooperation Council (GCC) countries and the Middle East region. This book gathers the proceedings of the SDMA 2018. All papers were peer-reviewed based on a strict policy concerning the originality, significance to the area, scientific vigor and quality of the contribution, and address the following research areas.• Applications: Applications of data mining in domains including databases,