Knowledge actionability: satisfying technical and business interestingness

Longbing Cao; Dan Luo; Chengqi Zhang

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Knowledge actionability: satisfying technical and business interestingness

机译：知识可操作性：满足技术和业务兴趣

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Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs.

机译：传统上，主要通过开发和提高技术兴趣来研究知识的可操作性。最近，有关技术主观兴趣和面向业务的利润挖掘的初步工作具有广阔的潜力，而弥合技术重要性和业务期望之间的差距是一项长期任务。在本文中，我们提出了一种用于衡量知识可操作性的双向重要性框架，该框架突出了技术兴趣和特定领域的期望。我们进一步开发了一种模糊的兴趣聚集机制，以生成平衡技术和商业利益的最终最终模式集。现实生活中的数据挖掘应用程序表明，所提出的知识可操作性框架可以补充技术兴趣，同时满足实际用户需求。

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来源
《International Journal of Business Intelligence and Data Mining》 |2007年第4期|p.496-514|共19页
作者
Longbing Cao; Dan Luo; Chengqi Zhang;
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作者单位

Faculty of Information Technology, University of Technology, Sydney, Australia;

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原文格式 PDF
正文语种 eng
中图分类贸易经济;
关键词
data mining; actionable knowledge; technical interestingness; business decision making; domain driven data mining;

机译：数据挖掘;可操作的知识;技术兴趣;业务决策;领域驱动的数据挖掘;

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Knowledge actionability: satisfying technical and business interestingness

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