Mining frequent closed patterns in pointset databases

In this paper, we proposed an efficient algorithm, called PCP-Miner (Pointset Closed Pattern Miner), for mining frequent closed patterns from a pointset database, where a pointset contains a set of points. Our proposed algorithm consists of two phases. First, we find all frequent patterns of length two in the database. Second, for each pattern found in the first phase, we recursively generate frequent closed patterns by a frequent pattern tree in a depth-first search manner. Since the PCP-Miner does not generate unnecessary candidates, it is more efficient and scalable than the modified Apriori, SASMiner and MaxGeo. The experimental results show that the PCP-Miner algorithm outperforms the comparing algorithms by more than one order of magnitude.