Generalized Association Rule Mining Algorithm with Multiple Minimum Supports Based on Frequent Pattern Tree
A generalized association rule with multiple minimum supports mining algorithm based on pattern growth mining paradigm called CFP-tax,is proposed by employing the MIS-tree to store the information about frequent patterns,which avoids candidate generation and costly database scans and efficiently finds all the frequent item sets in the database.The performance of the algorithm is evaluated based on synthetic datasets,and the results show that our algorithm significantly outperforms the classic MMS-Cumulate algorithm.