000 03727nam a22005415i 4500
001 978-1-4419-5737-5
003 DE-He213
005 20140220084508.0
007 cr nn 008mamaa
008 100301s2010 xxu| s |||| 0|eng d
020 _a9781441957375
_9978-1-4419-5737-5
024 7 _a10.1007/978-1-4419-5737-5
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aCao, Longbing.
_eauthor.
245 1 0 _aDomain Driven Data Mining
_h[electronic resource] /
_cby Longbing Cao, Philip S. Yu, Chengqi Zhang, Yanchang Zhao.
250 _aFirst.
264 1 _aBoston, MA :
_bSpringer US,
_c2010.
300 _aXIII, 237p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aInformation storage and retrieval systems.
650 0 _aInformation systems.
650 0 _aManagement information systems.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aBusiness Information Systems.
650 2 4 _aInformation Systems Applications (incl.Internet).
650 2 4 _aInformation Storage and Retrieval.
700 1 _aYu, Philip S.
_eauthor.
700 1 _aZhang, Chengqi.
_eauthor.
700 1 _aZhao, Yanchang.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441957368
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-5737-5
912 _aZDB-2-SCS
999 _c110515
_d110515