MINING ASSOCIATION RULES TO EVALUATE CONSUMER PERCEPTION: A NEW FP-TREE APPROACH


Nandini Das, Avishek Ghosh, Prasun Das

Abstract: Association rule mining finds in teresting relationships among large set of data items. While finding the impor tant (or, frequent) relations from the set of consumer survey data, a modified algorithm based on frequent pattern growth is developed in this work. The sensitivity of support and confidence used for rule mining on the data is tested. The interaction between the order of the attributes and the confid ence used is observed in terms of the number of rules mined. Th e impact of the product features on the level of consumer perception is thoroughly studied.

Keywords: Data mining, Association rule mi ning, Itemset, Frequent pattern tree, Support, Confid ence, Order, Cons umer satisfaction

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Recieved: 12.01.2011  Accepted: 10.04.2011  UDC: 005-6

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