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008			121126s2013 fluad sb 001 0 eng d
020			_a9781439854334 (ebook : PDF)
040			_aBD-DhSAU _cBD-DhSAU
090			_aQA76.9.D343 _bC65 2013
092			_a006.312 _bC764
245	0	0	_aContrast data mining _h[electronic resource] : _bconcepts, algorithms, and applications / _cedited by Guozhu Dong and James Bailey.
260			_aBoca Raton : _bCRC Press, _c2013.
300			_axxiv, 410 p. : _bill.
490	1		_aChapman & Hall/CRC data mining and knowledge discovery series
500			_a"A Chapman & Hall book."
504			_aIncludes bibliographical references (p. 363-402) and index.
505	0		_a1. Preliminaries and statistical contrast measures -- 2. Contrast mining algorithms -- 3. Generalized contrasts, emerging data cubes, and rough sets -- 4. Contrast mining for classification & clustering -- 5. Contrast mining for bioinformatics and chemoinformatics -- 6. Contrast mining for special domains -- 7. Survey of other papers.
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"-- _cProvided by publisher.
530			_aAlso available in print edition.
538			_aMode of access: World Wide Web.
650		0	_aContrast data mining.
655		7	_aElectronic books. _2lcsh
700	1		_aDong, Guozhu, _d1957- _eeditor of compilaton.
700	1		_aBailey, James, _d1971 June 30-
776	1		_z9781439854327 (hardback : acid-free paper)
830		0	_aChapman & Hall/CRC data mining and knowledge discovery series.
856	4	0	_uhttp://marc.crcnetbase.com/isbn/9781439854334 _qapplication/PDF
999			_c12153 _d12152