J.M. Saquer (USA)
Data and Text Mining, Clustering, Concept Lattice, Frequent Itemsets, Formal Concept Analysis, Machine Learning
Clustering is a well studied problem in data mining. Most clustering research has focused on generating disjoint clus ters with a recently increasing interest in overlapping clus tering [1, 2]. Concept lattices from Formal Concept Anal ysis provide a convenient and relatively easy way for over lapping clustering. On the other hand, disjoint clustering with concept lattices is an open problem [3]. In this paper, we present a solution to this open problem. Our solution uses frequent itemsets from association rule mining.
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