Data Mining Foundations And Practice
Download Data Mining Foundations And Practice full books in PDF, epub, and Kindle. Read online free Data Mining Foundations And Practice ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Author | : Tsau Young Lin |
Publisher | : Springer Science & Business Media |
Total Pages | : 562 |
Release | : 2008-08-20 |
Genre | : Mathematics |
ISBN | : 354078487X |
The IEEE ICDM 2004 workshop on the Foundation of Data Mining and the IEEE ICDM 2005 workshop on the Foundation of Semantic Oriented Data and Web Mining focused on topics ranging from the foundations of data mining to new data mining paradigms. The workshops brought together both data mining researchers and practitioners to discuss these two topics while seeking solutions to long standing data mining problems and stimul- ing new data mining research directions. We feel that the papers presented at these workshops may encourage the study of data mining as a scienti?c ?eld and spark new communications and collaborations between researchers and practitioners. Toexpressthevisionsforgedintheworkshopstoawiderangeofdatam- ing researchers and practitioners and foster active participation in the study of foundations of data mining, we edited this volume by involving extended and updated versions of selected papers presented at those workshops as well as some other relevant contributions. The content of this book includes st- ies of foundations of data mining from theoretical, practical, algorithmical, and managerial perspectives. The following is a brief summary of the papers contained in this book.
Author | : Ian H. Witten |
Publisher | : Elsevier |
Total Pages | : 665 |
Release | : 2011-02-03 |
Genre | : Computers |
ISBN | : 0080890369 |
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
Author | : Mohammed J. Zaki |
Publisher | : Cambridge University Press |
Total Pages | : 607 |
Release | : 2014-05-12 |
Genre | : Computers |
ISBN | : 0521766338 |
A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
Author | : Dawn E. Holmes |
Publisher | : |
Total Pages | : 0 |
Release | : |
Genre | : |
ISBN | : |
Author | : Tsau Young Lin |
Publisher | : Springer Science & Business Media |
Total Pages | : 400 |
Release | : 2005-09-02 |
Genre | : Computers |
ISBN | : 9783540262572 |
"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.
Author | : Dawn E. Holmes |
Publisher | : Springer Science & Business Media |
Total Pages | : 257 |
Release | : 2011-11-09 |
Genre | : Technology & Engineering |
ISBN | : 3642232418 |
There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.
Author | : K. P. SOMAN |
Publisher | : PHI Learning Pvt. Ltd. |
Total Pages | : 419 |
Release | : 2006-01-01 |
Genre | : Computers |
ISBN | : 8120328973 |
Data Mining is an emerging technology that has made its way into science, engineering, commerce and industry as many existing inference methods are obsolete for dealing with massive datasets that get accumulated in data warehouses. This comprehensive and up-to-date text aims at providing the reader with sufficient information about data mining methods and algorithms so that they can make use of these methods for solving real-world problems. The authors have taken care to include most of the widely used methods in data mining with simple examples so as to make the text ideal for classroom learning. To make the theory more comprehensible to the students, many illustrations have been used, and this in turn explains how certain parameters of interest change as the algorithm proceeds. Designed as a textbook for the undergraduate and postgraduate students of computer science, information technology, and master of computer applications, the book can also be used for MBA courses in Data Mining in Business, Business Intelligence, Marketing Research, and Health Care Management. Students of Bioinformatics will also find the text extremely useful. CD-ROM INCLUDE’ The accompanying CD contains Large collection of datasets. Animation on how to use WEKA and ExcelMiner to do data mining.
Author | : Tsau Young Lin |
Publisher | : Springer Science & Business Media |
Total Pages | : 398 |
Release | : 2005-11-03 |
Genre | : Mathematics |
ISBN | : 9783540283157 |
Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for real-world problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.
Author | : Dawn E. Holmes |
Publisher | : Springer Science & Business Media |
Total Pages | : 341 |
Release | : 2011-11-09 |
Genre | : Technology & Engineering |
ISBN | : 3642231667 |
There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.
Author | : Andrea Ahlemeyer-Stubbe |
Publisher | : John Wiley & Sons |
Total Pages | : 323 |
Release | : 2014-03-31 |
Genre | : Mathematics |
ISBN | : 1118763378 |
Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.