Data Mining Foundations And Practice

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Data Mining Foundations And Practice

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Author : Tsau Young Lin
language : en
Publisher: Springer
Release Date : 2008-08-17

Data Mining Foundations And Practice written by Tsau Young Lin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-17 with Technology & Engineering categories.

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.

Principles Of Data Mining

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Author : Max Bramer
language : en
Publisher: Springer
Release Date : 2016-11-09

Principles Of Data Mining written by Max Bramer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-09 with Computers categories.

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 are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.

Practical Applications Of Data Mining

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Author : Sang Suh
language : en
Publisher: Jones & Bartlett Publishers
Release Date : 2012

Practical Applications Of Data Mining written by Sang Suh and has been published by Jones & Bartlett Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.

Introduction to data mining -- Association rules -- Classification learning -- Statistics for data mining -- Rough sets and bayes theories -- Neural networks -- Clustering -- Fuzzy information retrieval.

Foundations Of Data Science

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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23

Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Data Mining Foundations And Intelligent Paradigms

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Author : Dawn E. Holmes
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-11-09

Data Mining Foundations And Intelligent Paradigms written by Dawn E. Holmes and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-09 with Computers categories.

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.

Data Mining And Analysis

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Author : Mohammed J. Zaki
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-12

Data Mining And Analysis written by Mohammed J. Zaki and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-12 with Computers categories.

A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

Data Mining

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Author : Ian H. Witten
language : en
Publisher: Elsevier
Release Date : 2011-02-03

Data Mining written by Ian H. Witten and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-03 with Computers categories.

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

Design And Analysis Of Learning Classifier Systems

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Author : Jan Drugowitsch
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-05-30

Design And Analysis Of Learning Classifier Systems written by Jan Drugowitsch and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-05-30 with Computers categories.

This book is probably best summarized as providing a principled foundation for Learning Classi?er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de?nition – derived from machine learning – of “a good set of cl- si?ers”, based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi?ers using that de?nition as a ?tness criterion, seeing ifthe setprovidesa goodsolutionto twodi?erent function approximation problems. It appears to, meaning that in some sense his de?nition of “good set of classi?ers” (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi?ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.

Computer And Information Science

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Author : Roger Lee
language : en
Publisher: Springer
Release Date : 2008-05-07

Computer And Information Science written by Roger Lee and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-05-07 with Computers categories.

The 7th IEEE/ACIS Conference and the 2nd IEEE/ACIS Workshop on e-Activity (IWEA 2008) featured researchers from around the world. The conference organizers selected 23 outstanding papers for this volume of Springer’s Studies in Computational Intelligence.

Communications And Discoveries From Multidisciplinary Data

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Author : Shuichi Iwata
language : en
Publisher: Springer
Release Date : 2008-08-17

Communications And Discoveries From Multidisciplinary Data written by Shuichi Iwata and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-17 with Technology & Engineering categories.

This book collects selected papers by authors for CODATA 2006, which are relevant to the acquisition of knowledge and the assessment of risk and opportunity that comes from combining data from a number of different disciplines.