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Titlebook: Data Mining for Business Applications; Longbing Cao,Philip S. Yu,Huaifeng Zhang Book 2009 Springer-Verlag US 2009 Business Decision Making

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書目名稱Data Mining for Business Applications
編輯Longbing Cao,Philip S. Yu,Huaifeng Zhang
視頻videohttp://file.papertrans.cn/263/262947/262947.mp4
概述Presents knowledge, techniques and case studies to bridge the gap between business expectations and research outputs.Explores new research issues in data mining, including trust, organizational and so
圖書封面Titlebook: Data Mining for Business Applications;  Longbing Cao,Philip S. Yu,Huaifeng Zhang Book 2009 Springer-Verlag US 2009 Business Decision Making
描述.Data Mining for Business Applications. presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business..
出版日期Book 2009
關(guān)鍵詞Business Decision Making; Business Intelligence; Clustering; Domain Driven Data Mining; Domain Knowledge
版次1
doihttps://doi.org/10.1007/978-0-387-79420-4
isbn_softcover978-1-4419-4635-5
isbn_ebook978-0-387-79420-4
copyrightSpringer-Verlag US 2009
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On Mining Maximal Pattern-Based Clustersion systems and target marketing systems in e-business. However, pattern-based clustering in large databases is still challenging. On the one hand, there can be a huge number of clusters and many of them can be redundant and thus make the pattern-based clustering ineffective. On the other hand, the
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Role of Human Intelligence in Domain Driven Data Miningeparation, modeling, evaluation and deployment. Various data mining tasks are dependent on the human user for their execution. These tasks and activities that require human intelligence are not amenable to automation like tasks in other phases such as data preparation or modeling are. Nearly all Dat
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Ontology Mining for Personalized Search provide a satisfactory solution for this challenge, because there exists a lot of uncertainties in the local information repositories. In this chapter, we introduce ontology mining, a new methodology, for solving this challenging issue, which aims to discover interesting and useful knowledge in dat
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978-1-4419-4635-5Springer-Verlag US 2009
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Longbing Cao,Philip S. Yu,Huaifeng ZhangPresents knowledge, techniques and case studies to bridge the gap between business expectations and research outputs.Explores new research issues in data mining, including trust, organizational and so
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Large-Scale Interconnected Systems,igm shift from ‘data mining’ to ‘knowledge discovery’, we believe much more thorough efforts are essential for promoting the wide acceptance and employment of knowledge discovery in real-world smart decision making. To this end, we expect a new paradigm shift from ‘data-centered knowledge discovery’
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