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DOI: http://dx.doi.org/10.22266/ijies2017.0228.19

Cyclic Repeated Patterns in Sequential Pattern Mining Based on the Fuzzy C-Means Clustering and Association Rule Mining Technique

Author(s):

Ramani Selvanambi1*,Jaisankar Natarajan1


Affiliations:

1Vellore Institute of Technology University, Vellore, India







Abstract:

The main aim of the proposed method is to remove cyclic repeated patterns in sequential pattern mining. Initially the input dataset is fed to the clustering process, in which fuzzy c means clustering algorithm is used to cluster the available data based on the similar sequential pattern. This approach is able to mine the patterns with the help of association rule mining, here two major tasks are present one is frequent item set generation and rule generation. In frequent item set generation, support and confidence value is evaluated for each pattern. Based on that, the rules are generated in rule generation. After the mining process, the threshold is fixed and based on that repeated cyclic patterns are removed and stored to the database. The performance of the suggested method is evaluated by means of execution time, memory and database difference ratio. The implementation is done with the help of JAVA platform.


Keywords:

Sequential pattern mining, Fuzzy C-means clustering, Association rule mining, Frequent item set generation, Rule generation.


Full Text:




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