000			02105cam a2200277zu 4500
001			88811757
003			FRCYB88811757
005			20250107210809.0
006			m o d
007			cr un
008			250107s2012 fr | o|||||0|0|||eng d
020			_a9780123969637
035			_aFRCYB88811757
040			_aFR-PaCSA _ben _c _erda
100	1		_aZhao, Yanchang
245	0	1	_aR and Data Mining _bExamples and Case Studies _c['Zhao, Yanchang']
264		1	_bElsevier Science _c2012
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aZhao, Yanchang
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88811757 _qtext/html _a
520			_aR and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work
999			_c60988 _d60988