Frequent Itemsets Mining Based on Concept Lattice and Sliding Window

Abstract

In this paper, a frequent itemsets mining algorithm of data stream based on concept lattice and sliding window is presented. This algorithm mines frequent concepts for new inflowing basic window in batches in a sliding window and generates concept lattice Hasse diagram. With introduction into small support degreeand error factorto do the pruning operations for non-frequent concept node, each connection point in the Hasse diagram contains the information of frequent itemsets and support degree. As the generation of Hasse diagram in the new basic windows, we integrate concept lattice vertically with the generated Hasse diagram and sliding window, and ultimately output all frequent itemsets through scanning all the graph nodes of Hasse diagram graph. The experimental results show that the proposed algorithm has a good performance.

DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3140