Issue No. 03 - March (2013 vol. 25)

ISSN: 1041-4347

pp: 690-703

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.260

Byron J. Gao , Texas State University - San Marcos, San Marcos

Martin Ester , Simon Fraser University, Burnaby

Hui Xiong , Rutgers, the State University of New Jersey, Newark

Jin-Yi Cai , University of Wisconsin - Madison, Madison

Oliver Schulte , Simon Fraser University, Burnaby

ABSTRACT

In this paper, we introduce and study the minimum consistent subset cover (MCSC) problem. Given a finite ground set X and a constraint t, find the minimum number of consistent subsets that cover X, where a subset of X is consistent if it satisfies t. The MCSC problem generalizes the traditional set covering problem and has minimum clique partition (MCP), a dual problem of graph coloring, as an instance. Many common data mining tasks in rule learning, clustering, and pattern mining can be formulated as MCSC instances. In particular, we discuss the minimum rule set (MRS) problem that minimizes model complexity of decision rules, the converse k-clustering problem that minimizes the number of clusters, and the pattern summarization problem that minimizes the number of patterns. For any of these MCSC instances, our proposed generic algorithm CAG can be directly applicable. CAG starts by constructing a maximal optimal partial solution, then performs an example-driven specific-to-general search on a dynamically maintained bipartite assignment graph to simultaneously learn a set of consistent subsets with small cardinality covering the ground set.

INDEX TERMS

Data mining, Pattern recognition, Complexity theory, Minimization, Decision trees, Graph coloring, Clustering algorithms, pattern summarization, Minimum consistent subset cover, set covering, graph coloring, minimum clique partition, minimum star partition, minimum rule set, converse k-clustering

CITATION

Byron J. Gao, Martin Ester, Hui Xiong, Jin-Yi Cai, Oliver Schulte, "The Minimum Consistent Subset Cover Problem: A Minimization View of Data Mining",

*IEEE Transactions on Knowledge & Data Engineering*, vol. 25, no. , pp. 690-703, March 2013, doi:10.1109/TKDE.2011.260CITATIONS

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