An efficient algorithm for mining closed weighted frequent pattern over data streams

Abstract

Weighted frequent pattern mining is suggested to discover more important frequent pattern by considering different weights of each item, and closed frequent pattern mining can reduces the number of frequent patterns and keep sufficient result information. In this paper, we propose an efficient algorithm DS_CWFP to mine closed weighted frequent pattern mining over data streams. We present an efficient algorithm based on sliding window and can discover closed weighted frequent pattern from the recent data. A new efficient DS_CWFP data structure is used to dynamically maintain the information of transactions and also maintain the closed weighted frequent patterns has been found in the current sliding window. Three optimization strategies are present. The detail of the algorithm DS_CWFP is also discussed. Experimental studies are performed to evaluate the good effectiveness of DS_CWFP.