A Tree-based Approach for Efficiently Mining Approximate Frequent Itemsets

—The strategies for mining frequent itemsets, which is the essential part of discovering association rules, have been widely studied over the last decade. In real-world datasets, it is possible to discover multiple fragmented patterns but miss the longer true patterns due to random noise and errors in the data. Therefore, a number of methods have been proposed recently to discover approximate frequent itemsets. However, a challenge of providing an efficient algorithm for solving this problem is how to avoid costly candidate generation and test. In this paper, an algorithm, named FP-AFI (FP-tree based Approximate Frequent Itemsets mining algorithm), is developed to discover approximate frequent itemsets from a FP-tree-like structure. We define a recursive function for getting the set of transactions which faulttolerant contain an itemset P. The patterns in the fault-tolerant supporting transactions of P are represented by the conditional AFP-trees of P. Moreover, to avoid re-construct...

• Type: Journal
• Year: 2010
• Where: RCIS
• Authors: Jia-Ling Koh, Yi-Lang Tu
• Keywords: Approximate Frequent Itemset, Candidate Itemset, Conditional Afp-trees, Information Technology, RCIS 2010