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Data Mining: A Knowledge Discovery Approach
Publisher:
Springer India
| Author:
Cios J. Krzysztof | Witold Pedrycz | Roman W. Swiniarski | Lukasz A. Kurgan
| Language:
English
| Format:
Paperback
Publisher:
Springer India
Author:
Cios J. Krzysztof | Witold Pedrycz | Roman W. Swiniarski | Lukasz A. Kurgan
Language:
English
Format:
Paperback
₹1,495 ₹1,270
Save: 15%
In stock
Ships within:
3-5 Days
In stock
ISBN:
SKU
9788184894998
Category Computer Engineering/IT Engineering
Category: Computer Engineering/IT Engineering
Page Extent:
621
Provides suite of exercises, Provides suite of exercises, Includes links to instructional presentations,Contains appendices of relevant mathematical material. This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribe the sequence in which data mining projects should be performed. Data Mining offers an authoritative treatment of all development phases from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes this book from other texts in the area. It concentrates on data preparation, clustering and association rule learning (required for processing unsupervised data), decision trees, rule induction algorithms, neural networks, and many other data mining methods, focusing predominantly on those which have proven successful in data mining projects.
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Description
Provides suite of exercises, Provides suite of exercises, Includes links to instructional presentations,Contains appendices of relevant mathematical material. This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribe the sequence in which data mining projects should be performed. Data Mining offers an authoritative treatment of all development phases from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes this book from other texts in the area. It concentrates on data preparation, clustering and association rule learning (required for processing unsupervised data), decision trees, rule induction algorithms, neural networks, and many other data mining methods, focusing predominantly on those which have proven successful in data mining projects.
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