000			02416nam a2200229Ia 4500
003			NULRC
005			20250520102804.0
008			250520s9999 xx 000 0 und d
020			_a9781523321438
040			_cNULRC
050			_aQA 76.9 .N67 2016
100			_aNorth, Matthew _eauthor
245		0	_aData mining for the masses / _cMatthew North
250			_aSECOND EDITION
260			_a[Place of publication not identifed] : _bCreateSpace Independent Publishing Platform, _cc2016
300			_axv, 296 pages : _bcolor illustrations ; _c28 cm.
500			_aData mining for the masses /, Data
505			_aSection 1. Data mining basics -- 1. Introduction to data mining and CRISP-DM -- 2. Organizational understanding and data understanding -- 3. Data preparation -- Section 2. Data mining models and methods -- 4. Correlational methods -- 5. Association rules -- 6. k-means clustering -- 7. Discriminant analysis, k-Nearest neighbors and Naive Bayes -- 8. Linear regression -- 9. Logistic regression -- 10. Decision trees -- 11. Neural networks -- 12. Text mining -- Section 3. Special considerations in data mining -- 13. Evaluation and deployment -- 14.Data mining ethics.
520			_aWe live in a world that generates tremendous amounts of data—more than ever before. In business, and in our personal lives, we use smartphones and tablets, web sites and watches; with dozens of apps and interfaces to shop, learn, entertain and inform. Businesses increasingly use technology to interact with consumers to provide marketing, customer service, product information and more. All of this technological activity generates data—data that can be useful in many ways. Data mining can help to identify interesting patterns and messages that exist, often hidden beneath the surface. In this modern age of information systems, it is easier than ever before to extract meaning from data. From classification to prediction, data mining can help. In Data Mining for the Masses, Second Edition, professor Matt North—a former risk analyst and software engineer at eBay—uses simple examples and clear explanations with free, powerful software tools to teach you the basics of data mining. In this Second Edition, implementations of these examples are offered in both an updated version of the RapidMiner software, and in the popular R Statistical Package.
650			_aDATA MINING
942			_2lcc _cBK
999			_c15351 _d15351