Fuzzy Frequent Pattern Mining by Compressing Large Databases


  • Surbhi K. Solanki PG Scholar Department of Information Technology, SVMIT, Bharuch
  • Jalpa T. Patel Department of Computer Science and Information Technology, SVMIT, Bharuch


Data Mining, Association Rule Mining, FP tree, Fuzzy FP tree, Directed Acyclic graph


Task of extracting useful and interesting knowledge from large data is called data mining. It has many
aspects like clustering, classification, anomaly detection, association rule mining etc. Among such data mining aspects,
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
behavior. 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 compressing the fuzzy FP tree which
confines itemsets into fuzzy regions with the membership value. The application of the compression mechanism results
in compact tree structure that reduces the computation time. The proposed method is compared with the conventional
method for analyzing the performance.



How to Cite

Surbhi K. Solanki, & Jalpa T. Patel. (2015). Fuzzy Frequent Pattern Mining by Compressing Large Databases. International Journal of Advance Engineering and Research Development (IJAERD), 2(7), 25–29. Retrieved from https://ijaerd.com/index.php/IJAERD/article/view/937