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A paradigm for detecting cycles in large data sets via fuzzy mining

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2 Author(s)
Buckley, J.P. ; Dept. of Comput. Sci., Dayton Univ., OH, USA ; Seitzer, J.

Traditional data mining algorithms identify associations in data that are not explicit. Cycle mining algorithms identify meta-patterns of these associations depicting inferences forming chains of positive and negative rule dependencies. This paper describes a formal paradigm for cycle mining using fuzzy techniques. To handle cycle mining of large data sets, which are inherently noisy, we present the α-cycle and β-cycle, the underlying formalism of the paradigm. Specifically, we show how α-cycles, desirable cycles, can be reinforced such that complete positive cycles are created, and how β-cycles can be identified and weakened. To accomplish this, we introduce the concept of Ω nodes that employ an alterability quantification, as well as using standard rule and node weighting (with associated thresholds)

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Knowledge and Data Engineering Exchange, 1999. (KDEX '99) Proceedings. 1999 Workshop on

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