Efficient Mining Maximal Variant Usage and Low Usage Biclusters in Discrete Function-Resource Matrix

Abstract

The functional layer is the pillar of the whole prognostics and health management system. Its effectiveness is the core of system task effectives. In this paper, we proposed a new bicluster mining algorithm: DoCluster algorithm, to effectively mine all biclusters with maximal variant usage rate and low usage rate in the discrete function-resource matrix. First, this algorithm constructs a sample weighted graph which includes all resource collections between both samples that satisfy the definition of variant usage rate or low usage rate; then, all biclusters with maximal variant usage rate and low usage rate satisfying the definition are mined using sample-growth and depth-first method in the constructed weighted graph. In order to improve the mining efficiency of the algorithm, DoCluster algorithm uses several pruning strategies to ensure the mining of maximal bicluster without candidate maintenance. The experimental results show our algorithm is more efficiently than other two algorithms.