Online Public Access Catalogue (OPAC)
Library,Documentation and Information Science Division

“A research journal serves that narrow

borderland which separates the known from the unknown”

-P.C.Mahalanobis


Image from Google Jackets

Data mining algorithms : explained using R / Pawel Cichosz.

By: Publication details: Chichester : John Wiley, c2015.Description: xxxi, 683 p. : illustrations ; 26 cmISBN:
  • 9781118332580
Subject(s): DDC classification:
  • 006.312 23 C568
Contents:
Part I. Preliminaries -- 1. Tasks -- 2. Basic statistics -- Part II. Classification -- 3. Decision trees -- 4. Naïve 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-- Index.
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.
Tags from this library: No tags from this library for this title. Log in to add tags.
Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 006.312 C568 (Browse shelf(Opens below)) Available 136284
Total holds: 0

Includes bibliographical references and index.

Part I. Preliminaries --
1. Tasks --
2. Basic statistics --
Part II. Classification --
3. Decision trees --
4. Naïve 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--
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.

There are no comments on this title.

to post a comment.
Library, Documentation and Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata 700108, INDIA
Phone no. 91-33-2575 2100, Fax no. 91-33-2578 1412, ksatpathy@isical.ac.in