Data Mining Applications With R Hardcover English by Yanchang Zhao - 30 December 2013

Yanchang Zhao

Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool.

English

Elsevier Science Publishing Co Inc

30 December 2013

514

A Senior Data Mining Analyst in Australia Government since 2009. Before joining public sector, he was an Australian Postdoctoral Fellow (Industry) in the Faculty of Engineering & Information Technology at University of Technology, Sydney, Australia. His research interests include clustering, association rules, time series, outlier detection and data mining applications and he has over forty papers published in journals and conference proceedings. He is a member of the IEEE and a member of the Institute of Analytics Professionals of Australia, and served as program committee member for more than thirty international conferences.

Yonghua Cen

Justin Cen

The book contains a wealth of modern material that should be covered in more depth in statistics courses: for example, missing data, outlier detection, missing imputation, correlation coefficient matrices, principles of model selection, text mining, and decision trees...The book has many hot and recent packages; many are written or have theory based on results developed since 2010.--MAA, April 23, 2014 "Zhao and Cen present 15 real-world applications of data mining with the open-source statistics software R. Each application covers the business background, and problems, data extraction and exploitation, data preprocessing, modeling, model evaluation, findings, and model deployment. They involve a diverse set of challenging problems in terms of data size, data type, data mining goals, and the methodologies and tools to carry out the analysis."--ProtoView, February 2014