An Efficient Technique for Frequent Item Set Mining in Time Series Data with Aid of AFCM

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Frequent item set mining from the database is a difficult task. Many techniques have been proposed to mine the frequent rules from the database, but it consider only the frequency value to decide whether the extracted rule is frequent are not. Hence we proposed one technique to overcome these problems by utilizing AFCM. Initially the time series database values are clustered using the AFCM technique. After that, the frequent item sets are mined from the clustered results by exploiting the sliding window technique. During the frequent item sets mining process, the item sets frequency and utility are considered and that item sets are frequent which are satisfying the utility and its consistency. The proposed technique is implemented in the MATLAB platform and its performance is evaluated using rainfall database.
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Adaptive Fuzzy C Means (AFCM); Cuckoo Search (CS); Frequent Item Set; Window Sliding; Consistency

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