Commercial Data Mining

• Author : David Nettleton
• Publisher : Morgan Kaufmann
• Release : 24 October 2021

Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business

• Author : Criminal and Juvenile Justice Information Task Force (Minn.)
• Publisher : Unknown
• Release : 24 October 2021

• Author : David Nettleton
• Publisher : Elsevier
• Release : 29 January 2014

Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business

• Author : J. C. W. Debuse
• Publisher : Unknown
• Release : 24 October 1997

• Author : Graham Williams
• Publisher : Springer Science & Business Media
• Release : 04 August 2011

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

• Author : Max Bramer
• Publisher : Springer
• Release : 09 November 2016

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used

• Author : Andrea Ahlemeyer-Stubbe,Shirley Coleman
• Publisher : John Wiley & Sons
• Release : 01 February 2018

Practical guide for deriving insight and commercial gain from data Monetising Data offers a practical guide for anyone working with commercial data but lacking deep knowledge of statistics or data mining. The authors — noted experts in the field — show how to generate extra benefit from data already collected and how to use it to solve business problems. In accessible terms, the book details ways to extract data to enhance business practices and offers information on important topics such as data

• Author : Anoop Singhal
• Publisher : Springer Science & Business Media
• Release : 06 April 2007

The application of data warehousing and data mining techniques to computer security is an important emerging area, as information processing and internet accessibility costs decline and more and more organizations become vulnerable to cyber attacks. These security breaches include attacks on single computers, computer networks, wireless networks, databases, or authentication compromises. This book describes data warehousing and data mining techniques that can be used to detect attacks. It is designed to be a useful handbook for practitioners and researchers in

• Author : S. Sumathi,S.N. Sivanandam
• Publisher : Springer Science & Business Media
• Release : 26 September 2006

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for

• Author : Vinodani Katiyar,Ina Kapoor
• Publisher : LAP Lambert Academic Publishing
• Release : 09 December 2015

Although neural networks are applied in a wide range of learning and commercial applications even though this method is not commonly used for data mining tasks. Companies have been collecting data for decades and have build up massive data warehouses and used data mining techniques to extract the value of data but only few practitioners have used neural networks in data mining though this method has proven successful in many situations. The main academic contributions of this study are summarized

• Author : Dorian Pyle
• Publisher : Morgan Kaufmann
• Release : 05 April 1999

Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has traditionally taken a backseat to the more alluring question of how best to extract meaningful knowledge. But without adequate preparation of your data, the return on the resources invested in mining is certain to be disappointing. Dorian Pyle corrects this imbalance. A twenty-five-year veteran of what has become the data mining industry, Pyle

• Author : Stéphane Tufféry
• Publisher : John Wiley & Sons
• Release : 23 March 2011

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such

• Author : Mahmoud Abou-Nasr,Stefan Lessmann,Robert Stahlbock,Gary M. Weiss
• Publisher : Springer
• Release : 13 November 2014

Data mining applications range from commercial to social domains, with novel applications appearing swiftly; for example, within the context of social networks. The expanding application sphere and social reach of advanced data mining raise pertinent issues of privacy and security. Present-day data mining is a progressive multidisciplinary endeavor. This inter- and multidisciplinary approach is well reflected within the field of information systems. The information systems research addresses software and hardware requirements for supporting computationally and data-intensive applications. Furthermore, it encompasses

• Author : Jaideep Vaidya,Christopher W. Clifton,Yu Michael Zhu
• Publisher : Springer Science & Business Media
• Release : 28 September 2006

Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area. Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform

• Author : Sholom M. Weiss,Nitin Indurkhya
• Publisher : Morgan Kaufmann
• Release : 24 October 1998

This book is the first technical guide to provide a complete, generalized road map for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.