Data Mining Foundations And Practice

Author : Tsau Young Lin,Ying Xie,Anita Wasilewska,Churn-Jung Liau
Publisher : Springer Science & Business Media
Page : 562 pages
File Size : 48,5 Mb
Release : 2008-08-20
Category : Mathematics
ISBN : 9783540784876

Get Book

Data Mining: Foundations and Practice by Tsau Young Lin,Ying Xie,Anita Wasilewska,Churn-Jung Liau Pdf

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 : Tsau Young Lin,Setsuo Ohsuga,Churn-Jung Liau,Xiaohua Hu,Shusaku Tsumoto
Publisher : Springer Science & Business Media
Page : 400 pages
File Size : 41,8 Mb
Release : 2005-09-02
Category : Computers
ISBN : 3540262571

Get Book

Foundations of Data Mining and Knowledge Discovery by Tsau Young Lin,Setsuo Ohsuga,Churn-Jung Liau,Xiaohua Hu,Shusaku Tsumoto Pdf

"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 : Wesley Chu,Tsau Young Lin
Publisher : Springer Science & Business Media
Page : 360 pages
File Size : 51,5 Mb
Release : 2005-09-15
Category : Computers
ISBN : 3540250573

Get Book

Foundations and Advances in Data Mining by Wesley Chu,Tsau Young Lin Pdf

With the growing use of information technology and the recent advances in web systems, the amount of data available to users has increased exponentially. Thus, there is a critical need to understand the content of the data. As a result, data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. In this carefully edited volume a theoretical foundation as well as important new directions for data-mining research are presented. It brings together a set of well respected data mining theoreticians and researchers with practical data mining experiences. The presented theories will give data mining practitioners a scientific perspective in data mining and thus provide more insight into their problems, and the provided new data mining topics can be expected to stimulate further research in these important directions.

Author : Dawn E. Holmes,Lakhmi C Jain
Publisher : Springer Science & Business Media
Page : 257 pages
File Size : 55,8 Mb
Release : 2011-11-09
Category : Technology & Engineering
ISBN : 9783642232411

Get Book

Data Mining: Foundations and Intelligent Paradigms by Dawn E. Holmes,Lakhmi C Jain Pdf

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 : Tsau Young Lin,Setsuo Ohsuga,Churn-Jung Liau,Xiaohua Hu
Publisher : Springer Science & Business Media
Page : 398 pages
File Size : 43,5 Mb
Release : 2005-11-03
Category : Mathematics
ISBN : 3540283153

Get Book

Foundations and Novel Approaches in Data Mining by Tsau Young Lin,Setsuo Ohsuga,Churn-Jung Liau,Xiaohua Hu Pdf

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 : Max Bramer
Publisher : Springer
Page : 526 pages
File Size : 42,9 Mb
Release : 2016-11-09
Category : Computers
ISBN : 9781447173076

Get Book

Principles of Data Mining by Max Bramer Pdf

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.

Author : Mohammed J. Zaki,Wagner Meira
Publisher : Cambridge University Press
Page : 607 pages
File Size : 42,7 Mb
Release : 2014-05-12
Category : Computers
ISBN : 9780521766333

Get Book

Data Mining and Analysis by Mohammed J. Zaki,Wagner Meira Pdf

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

Author : Andrea Ahlemeyer-Stubbe,Shirley Coleman
Publisher : John Wiley & Sons
Page : 323 pages
File Size : 43,8 Mb
Release : 2014-03-31
Category : Mathematics
ISBN : 9781118763377

Get Book

A Practical Guide to Data Mining for Business and Industry by Andrea Ahlemeyer-Stubbe,Shirley Coleman Pdf

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.

Author : Dawn E. Holmes,Lakhmi C. Jain
Publisher : Springer
Page : 336 pages
File Size : 49,5 Mb
Release : 2011-11-07
Category : Computers
ISBN : 3642231659

Get Book

Data Mining: Foundations and Intelligent Paradigms by Dawn E. Holmes,Lakhmi C. Jain Pdf

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 : Walter W. Piegorsch
Publisher : John Wiley & Sons
Page : 82 pages
File Size : 43,8 Mb
Release : 2015-08-17
Category : Mathematics
ISBN : 9781118619650

Get Book

Statistical Data Analytics by Walter W. Piegorsch Pdf

Statistical Data Analytics Statistical Data Analytics Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery Applications of data mining and ‘big data’ increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basics of data manipulation and visualization, and the central components of standard statistical inferences. The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics. Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced. Extensive examples using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others. Statistical Data Analytics: Focuses on methods critically used in data mining and statistical informatics. Coherently describes the methods at an introductory level, with extensions to selected intermediate and advanced techniques. Provides informative, technical details for the highlighted methods. Employs the open-source R language as the computational vehicle – along with its burgeoning collection of online packages – to illustrate many of the analyses contained in the book. Concludes each chapter with a range of interesting and challenging homework exercises using actual data from a variety of informatic application areas. This book will appeal as a classroom or training text to intermediate and advanced undergraduates, and to beginning graduate students, with sufficient background in calculus and matrix algebra. It will also serve as a source-book on the foundations of statistical informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.

Author : Dawn E. Holmes,Lakhmi C Jain
Publisher : Springer Science & Business Media
Page : 367 pages
File Size : 54,6 Mb
Release : 2012-01-12
Category : Technology & Engineering
ISBN : 9783642231513

Get Book

Data Mining: Foundations and Intelligent Paradigms by Dawn E. Holmes,Lakhmi C Jain Pdf

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 3: Medical, Health, Social, Biological and other Applications” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

Author : Jan Rauch,Zbigniew W. Ras,Petr Berka,Tapio Elomaa
Publisher : Springer
Page : 637 pages
File Size : 54,8 Mb
Release : 2009-08-27
Category : Computers
ISBN : 9783642041259

Get Book

Foundations of Intelligent Systems by Jan Rauch,Zbigniew W. Ras,Petr Berka,Tapio Elomaa Pdf

This book constitutes the refereed proceedings of the 18th International Symposium on Methodologies for Intelligent Systems, ISMIS 2009, held in Prague, Czech Republic, in September 2009. The 60 revised papers presented together with 4 plenary talks were carefully reviewed and selected from over 111 submissions. The papers are organized in topical sections on knowledge discovery and data mining, applications and intelligent systems in Medicine, logical and theoretical aspects of intelligent systems, text mining, applications of intelligent sysems in music, information processing, agents, machine learning, applications of intelligent systems, complex data, general AI as well as uncertainty.

Author : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 40,9 Mb
Release : 2011
Category : Data mining
ISBN : OCLC:1063829257

Get Book

Data Mining by Anonim Pdf

Author : Jacek Koronacki,Zbigniew W. Ras,Slawomir T. Wierzchon
Publisher : Springer
Page : 531 pages
File Size : 49,5 Mb
Release : 2009-11-27
Category : Computers
ISBN : 9783642051791

Get Book

Advances in Machine Learning II by Jacek Koronacki,Zbigniew W. Ras,Slawomir T. Wierzchon Pdf

Professor Richard S. Michalski passed away on September 20, 2007. Once we learned about his untimely death we immediately realized that we would no longer have with us a truly exceptional scholar and researcher who for several decades had been inf- encing the work of numerous scientists all over the world - not only in his area of exp- tise, notably machine learning, but also in the broadly understood areas of data analysis, data mining, knowledge discovery and many others. In fact, his influence was even much broader due to his creative vision, integrity, scientific excellence and excepti- ally wide intellectual horizons which extended to history, political science and arts. Professor Michalski’s death was a particularly deep loss to the whole Polish sci- tific community and the Polish Academy of Sciences in particular. After graduation, he began his research career at the Institute of Automatic Control, Polish Academy of Science in Warsaw. In 1970 he left his native country and hold various prestigious positions at top US universities. His research gained impetus and he soon established himself as a world authority in his areas of interest – notably, he was widely cons- ered a father of machine learning.

Author : Xin-She Yang
Publisher : Academic Press
Page : 188 pages
File Size : 54,9 Mb
Release : 2019-07-15
Category : Mathematics
ISBN : 9780128172162

Get Book

Introduction to Algorithms for Data Mining and Machine Learning by Xin-She Yang Pdf

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages