C. O. Shields

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Applications in information security, data mining, e-commerce, information retrieval and network management require the analysis of large graphs in order to discover homogeneous groupings of rows and columns, called cross associations. We show that finding an optimal cross association is NP-complete. Furthermore, we give a heuristic algorithm with an O(n 4)(More)
A technique for clustering data by common attribute values involves grouping rows and columns of a binary matrix to make the minimum number of submatrices all 1's. As binary matrices can be viewed as adjacency matrices of bipartite graphs, the problem is equivalent to partitioning a bipartite graph into the smallest number of complete bipartite sub-graphs(More)
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