Discovering novel knowledge using granule mining

Liu, Bin, Li, Yuefeng, & Tian, Yu-Chu (2012) Discovering novel knowledge using granule mining. In Lecture Notes in Computer Science [Rough Sets and Current Trends in Computing: 8th International Conference], Springer, Chengdu, China, pp. 380-387.

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This paper presents an extended granule mining based methodology, to effectively describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other granules, it provides a kind of novel knowledge in databases. We also provide an algorithm to implement the proposed methodology. The experiments conducted to characterize a real network traffic data collection show that the proposed concepts and algorithm are promising.

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89 since deposited on 04 May 2012
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ID Code: 50084
Item Type: Conference Paper
Refereed: Yes
Keywords: Granule mining, rough set, decision rule, association rule
DOI: 10.1007/978-3-642-32115-3_45
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200) > Computation Theory and Mathematics not elsewhere classified (080299)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 (please consult the author).
Deposited On: 04 May 2012 05:43
Last Modified: 15 Feb 2013 20:40

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