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Data mining algorithms explained using R / Paweł Cichosz

By: Cichosz, Pawel [author]Material type: Text

TextPublication details: United Kingdom : John Wiley & Sons Inc., c2015Description: xxxi, 683 pages ; illustrations ; 26 cmISBN: 9781118332580Subject(s): DATA MINING | COMPUTER ALGORITHMS | R (COMPUTER PROGRAM LANGUAGE)LOC classification: QA 76.9 .D343 .C53 2015

Contents:

Part I. Preliminaries -- 1. Tasks -- 2. Basic statistics -- Part II. Classification -- 3. Decision trees -- 4. Naèive Bayes classifier -- 5. Linear classification -- 6. Misclassification costs -- 7. Classification model evaluation -- Part III. Regression -- 8. Linear regression -- 9. Regression trees -- 10. Regression model evaluation -- Part IV. Clustering -- 11. (Dis)similarity measures -- 12. k-Centers clustering -- 13. Hierarchical clustering -- 14. Clustering model evaluation -- Part V. Getting better models -- 15. Model ensembles -- 16. Kernel methods -- 17. Attribute transformation -- 18. Discretization -- 19. Attribute selection.

Summary: Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

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Item type	Current library	Home library	Collection	Shelving location	Call number	Copy number	Status	Date due	Barcode
Books	LRC - Graduate Studies	National University - Manila	Gen. Ed. - CCIT	General Circulation	GC QA 76.9.D343 .C53 2015 (Browse shelf (Opens below))	c.1	Available		NULIB000011358

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Includes bibliographical references and index.

Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

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