TY  - BOOK
AU  - Cichosz,Pawel
TI  - Data mining algorithms: explained using R 
SN  - 9781118332580
U1  - 006.312 
PY  - 2015///
CY  - London
PB  - Wiley& Sons
KW  - Data mining
KW  - Computer algorithms
KW  - R (Computer program language)
KW  - MATHEMATICS / Probability & Statistics / General
N1  - Includes bibliographical references and index; Part I: Preliminaries
Covers learning tasks (classification, regression, clustering), basic statistics, visualization, and practical issues.

Part II: Classification
Discusses decision trees, Naïve Bayes, linear classifiers, misclassification costs, and model evaluation.

Part III: Regression
Explores linear regression, regression trees, and performance evaluation, with extensions beyond linearity.

Part IV: Clustering
Focuses on similarity measures, k-means, hierarchical clustering, and quality evaluation metrics.

Part V: Enhancing Models
Includes ensemble methods, kernel techniques (SVMs), attribute transformation, discretization, and selection.

Case Studies & Appendices
Real-world applications (e.g., Census data, crime analysis), R packages, datasets, and notations
ER  -