Efficient algorithm for extreme maximal biclique mining in cognitive frequency decision making

The cognitive radio is growing an important interest in wireless communication study. A cognitive radio ad hoc network may take a master-slave tree-based structure in some special applications. For a master node with limited communication capability, slave nodes usually use the same frequency to access a subnet managed by the master. Each slave node can acquire many frequencies for communication by a local spectrum sensing process. However, there may be no common set of frequencies available for every slave node. In this case, we should delete some slave nodes and keep other nodes staying in the subnet as many as possible. By mapping the node set and frequency set to be both parts of a bipartite graph respectively, the problem can be turned into a special case of searching for maximal bicliques. Based on a well-known LCM (Linear time Closed itemset Miner) algorithm which enumerates frequent item sets (maximal bicliques), and using a new technique in terms of dynamic thresholds, we have solved this problem in real time to meet requirements of most cases from our application. And we also improved the LCM algorithm by pruning more rows and columns of vertices in a bipartite graph and by mining more heuristic information about what vertices make others unclosed to achieve much better performance.