TY - GEN AU - Cichosz,Pawel TI - Data mining algorithms : explained using R SN - 9781118332580 U1 - 006.312 23 PY - 2015/// CY - Chichester PB - John Wiley KW - Data mining. KW - Computer algorithms KW - R (Computer program language) N1 - Includes bibliographical references and index; Part I. Preliminaries -- 1. Tasks -- 2. Basic statistics -- Part II. Classification -- 3. Decision trees -- 4. Naïve 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-- Index N2 - 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 -