000			03197cam a2200373 i 4500
001			10112819
003			OSt
005			20150408114135.0
006			m d
007			cr n
008			130803s2013 flua | b 001 0 eng d
020			_a9781439854327 (hardback : acidfree paper)
035			_a(WaSeSS)ssj0000755359
040			_aMa-Un _beng
082	0	0	_a006.312 _223 _bCOD
210	1	0	_aContrast data mining
245	0	0	_aContrast data mining _bconcepts, algorithms, and applications / _cedited by Guozhu Dong and James Bailey.
260			_aBoca Raton : _bCRC Press, Taylor & Francis Group _c2013.
300			_axxiv, 410 p. : _bill ; _c25 cm.
490	0		_aChapman & Hall/CRC data mining and knowledge discovery series
504			_aIncludes bibliographical references (pages 363-402) and index.
506			_aLicense restrictions may limit access.
520			_a"Preface Contrasting is one of the most basic types of analysis. Contrasting based analysis is routinely employed, often subconsciously, by all types of people. People use contrasting to better understand the world around them and the challenging problems they want to solve. People use contrasting to accurately assess the desirability of important situations, and to help them better avoid potentially harmful situations and embrace potentially beneficial ones. Contrasting involves the comparison of one dataset against another. The datasets may represent data of different time periods, spatial locations, or classes, or they may represent data satisfying different conditions. Contrasting is often employed to compare cases with a desirable outcome against cases with an undesirable one, for example comparing the benign and diseased tissue classes of a cancer, or comparing students who graduate with university degrees against those who do not. Contrasting can identify patterns that capture changes and trends over time or space, or identify discriminative patterns that capture differences among contrasting classes or conditions. Traditional methods for contrasting multiple datasets were often very simple so that they could be performed by hand. For example, one could compare the respective feature means, compare the respective attribute-value distributions, or compare the respective probabilities of simple patterns, in the datasets being contrasted. However, the simplicity of such approaches has limitations, as it is difficult to use them to identify specific patterns that offer novel and actionable insights, and identify desirable sets of discriminative patterns for building accurate and explainable classifiers"--
650		0	_aContrast data mining. _936346
650		7	_aBUSINESS & ECONOMICS / Statistics. _2bisacsh _936347
650		7	_aCOMPUTERS / Database Management / Data Mining. _2bisacsh _936348
650		7	_aCOMPUTERS / Programming / Algorithms. _2bisacsh _936349
700	1		_aDong, Guozhu, _d1957- _936350
700	1		_aBailey, James, _d1971 June 30- _936351
773		0	_tSTATSnetBASE
856	4	0	_uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio10112819 _zFull text available from STATSnetBASE
910			_aLibrary of Congress record
942			_2ddc _cBOOK
999			_c30257 _d264757