Efficient Mining of Large Maximal Bicliques from 3D Symmetric Adjacency Matrix

Abstract

In this paper, we address the problem of mining large maximal bicliques from a three-dimensional Boolean symmetric adjacency matrix. We propose CubeMiner-MBC algorithm which enumerates all the maximal bicliques satisfying the user-specified size constraints. Our algorithm enumerates all bicliques with less memory in depth first manner and does not store the previously computed patterns in the main memory for duplicate detection. To efficiently prune duplicate patterns, we have proposed a subtree pruning technique which reduces the total number of nodes that are processed and also reduces the total number of duplicate patterns that are generated. We have also incorporated several optimizations for efficient cutter generation and closure checking. Experiments involving several synthetic data sets show that our algorithm takes less running time than CubeMiner algorithm.