Parallel and Distributed Closed Regular Pattern Mining in Large Databases

Due to huge increase in the records and dimensions of available databases pattern mining in large databases is a challenging problem. A good number of parallel and distributed FP mining algorithms have been proposed for large and distributed databases based on frequency of item set. Not only the frequency, regularity of item also can be considered as emerging factor in data mining research. Current days closed itemset mining has gained lot of attention in data mining research. So far some algorithms have been developed to mine regular patterns, there is no algorithm exists to mine closed regular patterns in parallel and distributed databases. In this paper we introduce a novel method called PDCRP-method (Parallel and Distributed closed regular pattern) to discover closed regular patterns using vertical data format on large databases. This method works at each local processor which reduces inter processor communication overhead and getting high degree of parallelism generates complete set of closed regular patterns. Our experimental results show that our PDCRP method is highly efficient in large databases.