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001			285929
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007			cr nn 008mamaa
008			150903s2010 xxu| o |||| 0|eng d
020			_a9781441957375 _99781441957375
024	7		_a10.1007/9781441957375 _2doi
035			_avtls000338470
039		9	_a201509030815 _bVLOAD _c201404300346 _dVLOAD _y201402060911 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA76.9.D343
100	1		_aCao, Longbing. _eautor _9303863
245	1	0	_aDomain Driven Data Mining / _cby Longbing Cao, Philip S. Yu, Chengqi Zhang, Yanchang Zhao.
250			_aFirst.
264		1	_aBoston, MA : _bSpringer US, _c2010.
300			_axiii, 237 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aChallenges and Trends -- Methodology -- Ubiquitous Intelligence -- Knowledge Actionability -- AKD Frameworks -- Combined Mining -- Agent-Driven Data Mining -- Post Mining -- Mining Actionable Knowledge on Capital Market Data -- Mining Actionable Knowledge on Social Security Data -- Open Issues and Prospects -- Reading Materials.
520			_aIn the present thriving global economy a need has evolved for complex data analysis to enhance an organization’s production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the most active areas in information technologies. Domain Driven Data Mining offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. About this book: Enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence. Examines real-world challenges to and complexities of the current KDD methodologies and techniques. Details a paradigm shift from "data-centered pattern mining" to "domain driven actionable knowledge discovery" for next-generation KDD research and applications. Bridges the gap between business expectations and research output through detailed exploration of the findings, thoughts and lessons learned in conducting several large-scale, real-world data mining business applications Includes techniques, methodologies and case studies in real-life enterprise data mining Addresses new areas such as blog mining Domain Driven Data Mining is suitable for researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aYu, Philip S. _eautor _9303864
700	1		_aZhang, Chengqi. _eautor _9303865
700	1		_aZhao, Yanchang. _eautor _9314355
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781441957368
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4419-5737-5 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c285929 _d285929