Comparative Analysis of Non-Frequent Pattern Mining Approach
Karamjit Kaur, Rajeev Bedi, R.C.Gangwar
Keywords: Association rule mining, Data Mining, Frequent pattern Mining, Infrequent weighted itemset, Weighted mining.
ABSTRACT: Data mining has many aspects like clustering, classification, anomaly detection, association rule mining etc. Among such data mining tools, association rule mining has gained a lot of interest among the researchers. Some applications of association mining include analysis of stock database, mining of the web data, diagnosis in medical domain and analysis of customer behaviour. In past, many algorithms were developed by researchers for mining frequent itemsets but the problem is that it generates candidate itemsets. So, to overcome it tree based approach for mining frequent patterns were developed that performs the mining operation by constructing tree with item on its node that eliminates the disadvantage of most of the algorithms. The paper tries to address the problem of finding frequent itemset by determining the infrequent itemsets in a transaction which would reduce the computation time. The proposed algorithm is compared with the existing weighted mining algorithm for performance evaluation.
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