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Parallel Key Value Pattern Matching Model


R. Senthamil Selvi , Assistant Professor, Department of Computer Science Jamal Mohamed College (Autonomous), Tiruchirappalli; Dr. T. Abdul Razak, Associate Professor, Department of Computer Science Jamal Mohamed College (Autonomous), Tiruchirappalli


Data mining, FP Growth, Frequent Item Set Mining, Association rule Mining


Mining frequent itemsets from the huge transactional database is an important task in data mining. To find frequent itemsets in databases involves big decision in data mining for the purpose of extracting association rules. Association rule mining is used to find relationships among large datasets. Many algorithms were developed to find those frequent itemsets. This work presents a summarization and new model of parallel key value pattern matching model which shards a large-scale mining task into independent, parallel tasks. It produces a frequent pattern showing their capabilities and efficiency in terms of time consumption. It also avoids the high computational cost. It discovers the frequent item set from the database.

Other Details

Paper ID: IJSRDV2I8115
Published in: Volume : 2, Issue : 8
Publication Date: 01/11/2014
Page(s): 181-185

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