关键词: 频繁项集挖掘, 关联规则, 剪枝策略, 位图编码

Abstract:

Most of the frequent pattern mining algorithms based on the FP-growth idea have the disadvantages of complex construction rules and cumbersome support calculations. This paper proposes a Frequent Item set Mining algorithm（BCLFIM） based on Bitmap-Code List（BC-List） to improve this problem. Firstly, in this algorithm, a node coding model based on bitmap  representation is adopted to generate BC-tree, and the node information of BC-tree is used as the data structure to quickly obtain the node set of BC-List by bitwise operation, which can reduce complicated intersection operation and improve connection efficiency. Secondly, the search space for mining frequent patterns is reduced by using the superset equivalence and support count prune strategy. Experimental show that the algorithm has faster mining speed than FIN and DFIN algorithms.

Key words: frequent item mining, association rules, pruning strategy, bitmap encoding