Titlebook: Data Mining Methods for Knowledge Discovery; Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniars Book 1998 Springer Science+Business Media

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書目名稱Data Mining Methods for Knowledge Discovery被引頻次




書目名稱Data Mining Methods for Knowledge Discovery被引頻次學(xué)科排名




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書目名稱Data Mining Methods for Knowledge Discovery讀者反饋




書目名稱Data Mining Methods for Knowledge Discovery讀者反饋學(xué)科排名

Ventilator

0893-3405 nowledge discovery. This book first elaborates on thefundamentals of each of the data mining methods: rough sets, Bayesiananalysis, fuzzy sets, genetic algorithms, machine learning, neuralnetworks, and preprocessing techniques. The book then goes on tothoroughly discuss these methods in the setting

Antagonism

etidronate

Book 1998liography. ..Data Mining Methods for Knowledge Discovery. is intended forsenior undergraduate and graduate students, as well as a broadaudience of professionals in computer and information sciences,medical informatics, and business information systems.

Presbycusis

0893-3405 ensive bibliography. ..Data Mining Methods for Knowledge Discovery. is intended forsenior undergraduate and graduate students, as well as a broadaudience of professionals in computer and information sciences,medical informatics, and business information systems.978-1-4613-7557-9978-1-4615-5589-6Series ISSN 0893-3405

RAFF

https://doi.org/10.1007/978-3-031-57373-6he most representative examples of the principle of evolutionary computing. Owing to the generality of evolutionary computing and a lack of specific assumptions about a problem to be tackled, genetic algorithms are capable of dealing with a broad class of tasks in spite of their formulation and the nature of the optimization to be completed.

RAFF

Evolutionary Computing,he most representative examples of the principle of evolutionary computing. Owing to the generality of evolutionary computing and a lack of specific assumptions about a problem to be tackled, genetic algorithms are capable of dealing with a broad class of tasks in spite of their formulation and the nature of the optimization to be completed.

遠(yuǎn)地點(diǎn)

Fuzzy Sets,ets, and related concepts of shadowed sets and rough sets. We highlight differences between computing with fuzzy sets and probabilities. Furthermore, we exhaustively revisit a concept of information granularity as emerging in fuzzy sets that constitutes a key notion of efficient machinery of data mining.

guzzle

Bayesian Methods,ues of probability densities used in Bayesian inference. Finally the probabilistic neural network PNN, as a hardware implementation of kernel-based probability density and Bayesian classification, is discussed.

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