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 and business analytics with R / Johannes Ledolter.

By: Material type: TextTextPublication details: New Jersey : John Wiley, c2013.Description: xi, 351 p. : illustrations (some color) ; 25 cmISBN:
  • 9781118447147 (cloth)
Subject(s): DDC classification:
  • 006.312 23 L474
Contents:
1. Introduction-- 2. Processing the information and getting to know your data-- 3. Standard linear regression-- 4. Local polynomial regression: a nonparametric regression approach-- 5. Importance of parsimony in statistical modeling-- 6. Penalty-based variable selection in regression models with many parameters (LASSO)-- 7. Logistic regression-- 8. Binary classification, probabilities, and evaluating classification performance-- 9. Classification using a nearest neighbor analysis-- 10. The naive Bayesian analysis: a model for predicting a categorical response from mostly categorical predictor variables-- 11. Multinomial logistic regression-- 12. More on classification and a discussion on discriminant analysis-- 13. Decision trees-- 14. Further discussion on regression and classification trees, computer software, and other useful classification methods-- 15. Clustering-- 16. Market basket analysis: association rules and lift-- 17. Dimension reduction: factor models and principal components-- 18. Reducing the dimension in regressions with multicollinear inputs: principal components regression and partial least squares-- 19. Text as data: text mining and sentiment analysis-- 20. Network data-- Appendix A: Exercises-- Appendix B: References-- Index.
Summary: Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets.
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 L474 (Browse shelf(Opens below)) Available 135462
Books ISI Library, Kolkata 006.312 L474 (Browse shelf(Opens below)) Available C26295
Total holds: 0

Includes bibliographical references and index.

1. Introduction--
2. Processing the information and getting to know your data--
3. Standard linear regression--
4. Local polynomial regression: a nonparametric regression approach--
5. Importance of parsimony in statistical modeling--
6. Penalty-based variable selection in regression models with many parameters (LASSO)--
7. Logistic regression--
8. Binary classification, probabilities, and evaluating classification performance--
9. Classification using a nearest neighbor analysis--
10. The naive Bayesian analysis: a model for predicting a categorical response from mostly categorical predictor variables--
11. Multinomial logistic regression--
12. More on classification and a discussion on discriminant analysis--
13. Decision trees--
14. Further discussion on regression and classification trees, computer software, and other useful classification methods--
15. Clustering--
16. Market basket analysis: association rules and lift--
17. Dimension reduction: factor models and principal components--
18. Reducing the dimension in regressions with multicollinear inputs: principal components regression and partial least squares--
19. Text as data: text mining and sentiment analysis--
20. Network data--

Appendix A: Exercises--
Appendix B: References--
Index.

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets.

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