Parallel and Distributed Closed Regular Pattern Mining in Large Databases

Abstract 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

A technique for parallel share-frequent sensor pattern mining from wireless sensor networks

WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability

Minería de Reglas de Asociación en GPU

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