Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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Information Processing Letters
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WWW '99 Proceedings of the eighth international conference on World Wide Web
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Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
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ICDT '99 Proceedings of the 7th International Conference on Database Theory
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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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Machine Learning
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Discrete Applied Mathematics - The fourth international colloquium on graphs and optimisation (GO-IV)
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
A correspondence between maximal complete bipartite subgraphs and closed patterns
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Proceedings of the 17th ACM conference on Information and knowledge management
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Information Sciences: an International Journal
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
A continuous characterization of the maximum-edge biclique problem
Journal of Global Optimization
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Many real world applications rely on the discovery of maximal biclique subgraphs (complete bipartite subgraphs). However, existing algorithms for enumerating maximal bicliques are not very efficient in practice. In this paper, we propose an efficient algorithm to mine large maximal biclique subgraphs from undirected graphs. Our algorithm uses a divide-and-conquer approach. It effectively uses the size constraints on both vertex sets to prune unpromising bicliques and to reduce the search space iteratively during the mining process. The time complexity of the proposed algorithm is O(ndN), where n is the number of vertices, d is the maximal degree of the vertices and N is the number of maximal bicliques. Our performance study shows that the proposed algorithm outperforms previous work significantly.