TY  - BOOK
AU  - Cichosz,Pawel
TI  - Data mining algorithms: explained using R
SN  - 9781118332580 (hardback)
U1  - 006.312 23
PY  - 2015///
CY  - Chichester :     
PB  - John Wiley,   
KW  - Data mining
KW  - Computer algorithms
KW  - R (Computer program language)
KW  - MATHEMATICS / Probability & Statistics / General
N1  - 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
N2  - "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

ER  -