Cichosz, Pawel <br/><br/>
Data mining algorithms :  explained using R  

 - London  Wiley& Sons  2015 
 - 683p. 
<br/><br/>Includes bibliographical references and index. 
<br/><br/>Part I: Preliminaries<br/>Covers learning tasks (classification, regression, clustering), basic statistics, visualization, and practical issues.<br/><br/>Part II: Classification<br/>Discusses decision trees, Naïve Bayes, linear classifiers, misclassification costs, and model evaluation.<br/><br/>Part III: Regression<br/>Explores linear regression, regression trees, and performance evaluation, with extensions beyond linearity.<br/><br/>Part IV: Clustering<br/>Focuses on similarity measures, k-means, hierarchical clustering, and quality evaluation metrics.<br/><br/>Part V: Enhancing Models<br/>Includes ensemble methods, kernel techniques (SVMs), attribute transformation, discretization, and selection.<br/><br/>Case Studies & Appendices<br/>Real-world applications (e.g., Census data, crime analysis), R packages, datasets, and notations. 
<br/><br/><label>ISBN: </label>9781118332580 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Data mining.<br/>Computer algorithms.<br/>R (Computer program language)<br/>MATHEMATICS / Probability & Statistics / General.
<br/><br/><label>Dewey Class. No.: </label>006.312  / CIC/D