Abstract
This paper proposed an efficient bicluster mining algorithm: LowCluster, to effectively mine all the maximal constant-row biclusters with low usage rate in real-valued function–resource matrix. First, a sample weighted graph is constructed; it includes all resource collections between both samples that meet the definition of low usage rate; then, all the maximal constant-row biclusters with low usage rate are mined using sample-growth and depth-first method in the sample weighted graph. In order to improve the mining efficiency, LowCluster algorithm uses pruning strategy to ensure the mining of maximal bicluster without candidate maintenance. The experimental results show that LowCluster algorithm is more efficient than traditional constant-row biclusters mining algorithm.
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Acknowledgments
This paper is supported by Avionics Science Foundation (No. 20125552053), National Key Basic Research Program of China (No. 2014CB744900), and Graduate starting seed fund of Northwestern Polytechnical University (No. Z2013130).
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Wang, M., Zhang, L., Gu, Q., Wang, G. (2014). LowCluster: Efficient Mining Maximal Constant-Row Bicluster with Low Usage Rate in Function–Resource Matrix. In: Wang, J. (eds) Proceedings of the First Symposium on Aviation Maintenance and Management-Volume II. Lecture Notes in Electrical Engineering, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54233-6_7
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DOI: https://doi.org/10.1007/978-3-642-54233-6_7
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