Title

Using concept lattices for disjoint clustering

Abstract

Clustering is a well studied problem in data mining. Most clustering research has focused on generating disjoint clusters with a recently increasing interest in overlapping clustering [1, 2]. Concept lattices from Formal Concept Analysis provide a convenient and relatively easy way for over-lapping clustering. On the other hand, disjoint clustering with concept lattices is an open problem. In this paper, we present a solution to this open problem. Our solution uses frequent itemsets from association rule mining.

Department(s)

Computer Science

Document Type

Conference Proceeding

Keywords

Clustering, Concept Lattice, Data and Text Mining, Formal Concept Analysis, Frequent Itemsets, Machine Learning

Publication Date

12-1-2003

Journal Title

Proceedings of the IASTED International Conference on Information and Knowledge Sharing

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