000			01345nam a22001937a 4500
020			_a9781118332580
082	0	0	_a006.312 _bCIC/D
100	1		_aCichosz, Pawel
245	1	0	_aData mining algorithms : _bexplained using R
260			_aLondon _bWiley& Sons _c2015
300			_a683p.
504			_aIncludes bibliographical references and index.
505			_aPart 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.
650		0	_aData mining.
650		0	_aComputer algorithms.
650		0	_aR (Computer program language)
650		7	_aMATHEMATICS / Probability & Statistics / General.
942			_cBK
999			_c2378 _d2378