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Data mining algorithms: explained using R / Pawel Cichosz.

By: Material type: TextTextPublication details: Chichester : John Wiley, ©2015.Description: xxxi, 683pISBN:
  • 9781118332580 (hardback)
Subject(s): DDC classification:
  • 006.312 23 C568
Contents:
1. Tasks-- 2. Basic statistics-- 3. Decision trees-- 4. Naive Bayes classifier-- 5. Linear classification-- 6. Misclassification costs-- 7. Classification model evaluation-- 8. Linear regression-- 9. Regression trees-- 10. Regression model evaluation-- 11. (Dis)similarity measures-- 12. k-Centers clustering-- 13. Hierarchical clustering-- 14. Clustering model evaluation-- 15. Model ensembles-- 16. Kernel methods-- 20. Case studies-- A. Notation-- B. R packages-- C Datasets-- Index.
Summary: "This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--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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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 PC3424
Total holds: 0

Includes bibliographical references and index.

1. Tasks--
2. Basic statistics--
3. Decision trees--
4. Naive Bayes classifier--
5. Linear classification--
6. Misclassification costs--
7. Classification model evaluation--
8. Linear regression--
9. Regression trees--
10. Regression model evaluation--
11. (Dis)similarity measures--
12. k-Centers clustering--
13. Hierarchical clustering--
14. Clustering model evaluation--
15. Model ensembles--
16. Kernel methods--
20. Case studies--
A. Notation--
B. R packages--
C Datasets--
Index.

"This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--

"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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