Mining Closed Patterns in Pointset Databases
Date Issued
2008
Date
2008
Author(s)
Chen, Po-Yin
Abstract
With advance in mobile computing and positioning technologies, location-based services (LBS) have gained significant progress. By using these technologies, a large amount of pointsets can be collected in an LBS database where a pointset contains a set of points. Mining frequent pointset in a pointset database can help us understand the movement patterns of objects. In this thesis, we proposed a novel algorithm, PCP-Miner (Pointset Closed Pattern Miner), to mine frequent closed pointset patterns. 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 patterns by a frequent pattern tree in a depth-first search manner. During the process of pattern generation, we check whether the frequent patterns are closed or not. Since the PCP-Miner only needs to scan the database once and doesn’t generate unnecessary candidates, it is more efficient than the modified Apriori algorithm. The experiment results show that the PCP-Miner outperforms the modified Apriori by one order of magnitude in both synthetic and real data.
Subjects
data mining
pointset databases
closed patterns
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