Mining Closed Item sets from Tuple-Evolving Data Streams

Abstract

Frequent Itemset Mining is playing major role in extracting useful knowledge from data streams that are exhibiting high data flow. Studies in data streams shows that every incoming data is considered as new tuple which is considered as revised tuple in some applications called as tuple evolving data streams. Extracting redundant less knowledge from such kind of application helps in better decision making with new challenges.One of the issue is, due to incoming revised tuple, some of the frequent itemsets may turn to infrequent or previously ignore itemsets may become frequent. Other issue is result of FIM may be huge and redundant results.In this paper, we address solution to the problem by finding closed itemsets from tuple revision data streams. We propose an efficient approach MCST that uses compressed SlideTree data structure to maintain stream data,proposeHIS hash tableto maintain itemsets, and CIS tables to maintain closed id sets to improve search performance of HIS.

A genetic algorithm coupled with tree-based pruning for mining closed association rules

“…Experiments showed that the proposed algorithm outperformed contemporary algorithms in terms of run time. Closed itemsets were extracted from a data stream using a compressed SlideTree data structure [16]. The method proposed here removes tuples that get updated from the SlideTree thereby avoiding the need to visit the entire previous slide.…”