| Summary: |
"The versatile capabilities and large set of addon packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data mining with R: learning with case studies uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a handson approach that utilizes a series of detailed, realworld case studies: predicting algae blooms, predicting stock market returns, detecting fraudulent transactions, classifying microarray samples. With these case studies, the author supplies all necessary steps, code, and data. Resource: A supporting website mirrors the doit-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions"-- |