DCWFP-Miner: Mining closed weighted frequent patterns over data streams

Abstract

closed frequent pattern mining can reduces the number of frequent patterns and keep sufficient result information. In this paper, we discuss the closed weighted frequent pattern mining problem over data streams. Continuous, unbounded and high-speed data streams make closed weighted frequent pattern mining become a difficult task. We present an efficient algorithm DCWFP-Miner, which is based on sliding window and can discover closed weighted frequent pattern from the recent data. The right order of the closed and weighted frequent constraints is proved and 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. The detail of the algorithm DCWFP-Miner is also discussed. Experimental studies are performed to evaluate the good effectiveness of DCWFP-Miner.