Rare Association Rule Mining using Improved FP- Growth algorithm

Rare association rule refers to an association rule forming between frequent and rare items or among rare items. CFPgrowth approach is used to mine frequent patterns using multiple minimum support (minsup) values. This approach is an extension of FP-growth approach to multiple minsup values. This approach involves construction of MIS-tree and generating frequent patterns from the MIS-tree. The issue in CFP-growth is constructing the compact MIStree because CFP-growth considers certain items, which will generate neither frequent patterns nor rules. In this paper, we propose an efficient approach for constructing the compact MIS-tree. To do so, the proposed approach explores the notions “least minimum support” and “infrequent child node pruning. The proposed approach improves the performance over CFP-growth approach.