Cichosz, Pawel <br/><br/>
Data mining algorithms explained using R / 
Paweł Cichosz 
 - United Kingdom :  John Wiley & Sons Inc.,   c2015. 
 - xxxi, 683 pages ;   illustrations ;  26 cm. 
<br/><br/>Includes bibliographical references and index. 
<br/><br/>Part I. Preliminaries -- 1. Tasks -- 2. Basic statistics -- Part II. Classification -- 3. Decision trees -- 4. Naèive Bayes classifier -- 5. Linear classification -- 6. Misclassification costs -- 7. Classification model evaluation -- Part III. Regression -- 8. Linear regression -- 9. Regression trees -- 10. Regression model evaluation -- Part IV. Clustering -- 11. (Dis)similarity measures -- 12. k-Centers clustering -- 13. Hierarchical clustering -- 14. Clustering model evaluation -- Part V. Getting better models -- 15. Model ensembles -- 16. Kernel methods -- 17. Attribute transformation -- 18. Discretization -- 19. Attribute selection. 
<br/><br/> 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. 
<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/><br/><label>LC Class. No.: </label>QA 76.9 .D343 .C53 2015