infrequent weighted itemset mining using frequent pattern growth
Namita Dilip Ganjewar,
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:5 Issue:3 Year: 22 March,2015 Pages:384-394
Abstract
Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Frequent weighted itemsets represent correlations frequently holding in data in which items may weight differently. Infrequent Itemset mining is a variation of frequent itemset mining where it finds the uninteresting patterns i.e., it finds the data items which occurs very rarely. This seminar tackles the issue of discovering rare and weighted itemsets, i.e., the infrequent weighted itemset (IWI) mining problem. Two algorithms that perform IWI and Minimal IWI mining efficiently are presented. This new algorithm is based on the pattern-growth paradigm to find minimally infrequent itemsets. A minimally infrequent itemset has no subset which is also infrequent.
Kewords
Association rules, infrequent patterns, IWI support, FP Growth
Reference
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