Hemalatha, R; Krishnan, A; Senthamarai, C; Hemamalini, R(INFLIBNET Centre, February 2, 2005)
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Abstract:
Correlated pattern mining has become increasingly important recently as an alternative or
an augmentation of association rule mining. Though correlated pattern mining discloses
the correlation relationships among data objects and reduces significantly the number of
patterns produced by the association mining, it still generates quite a large number of
patterns. This paper proposes closed correlated pattern mining to reduce the number of the
correlated patterns produced without information loss. A new notion of the confidenceclosed
correlated patterns is proposed first, and then an efficient algorithm is present,
called CCMine, for mining those patterns. Confidence closed pattern mining reduces the
number of patterns by at least an order of magnitude. It also shows that CCMine outperforms
a simple method making use of the traditional closed pattern miner. Confidence-closed
pattern mining is a valuable approach to condensing correlated patterns.