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A Novel Enumeration Strategy of Maximal Bicliques from 3-Dimensional Symmetric Adjacency Matrix

Michael Raj Dominic Savio, Annamalai Sankar, Nataraj Ramaiya Vijayarajan


Several algorithms are available in the literature to find all maximal bicliques from an adjacency matrix. If these algorithms are applied directly on a symmetric adjacency matrix, all maximal bicliques will be generated twice. We propose a novel algorithm, S-Datapeeler, to enumerate all maximal bicliques only once in the context of 3-dimensional symmetric adjacency matrix i.e. zero duplicate patterns are generated. We have compared our results with DataPeeler in 3-D context. The DataPeeler algorithm generates all duplicate patterns, whereas the proposed algorithm completely eliminates all duplicate maximal bicliques. The proposed methodology results in 50% reduction in search space and thereby running time of S-Datapeeler is better for 3-D symmetric datasets.


Data Mining, Maximal Bicliques, Symmetric matrix, Algorithms

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