Data Mining (Lecture Notes in Physics)

Science & Math

Pedrycz, Witold

Cios, Krzysztof J.

Swiniarski, Roman W.

Physics

Springer

Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography. Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.

Data Mining Methods for Knowledge Discovery (The Springer International Series in Engineering and Computer Science, 458)

Reference

This comprehensive textbook on data mining details the unique steps of the knowledge discovery process - an industry standard that prescribes the sequence in which projects should be performed, from data understanding and preprocessing to deployment of the results.

Data Mining: A Knowledge Discovery Approach

Computers & Technology

Kurgan, Lukasz Andrzej

Cios, Krzysztof J. J.

Computer Science

Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography.  Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.

Data Mining Methods for Knowledge Discovery (The Springer International Series in Engineering and Computer Science)

The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, starting from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. This first volume of the Transactions on Rough Sets opens with an introductory article by Zdzislaw Pawlak, the originator of rough sets. Nine papers deal with rough set theory and eight are devoted to applications in various domains.

Transactions on Rough Sets I (Lecture Notes in Computer Science, 3100)

Peters, James F.

Grzymala-Busse, Jerzy W.

Kostek, Bozena

Skowron, Andrzej

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