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DSWFP: Efficient mining of weighted frequent pattern over data streams

By considering different weights of items, weighted frequent pattern (WFP) mining can find more important frequent patterns. However previous WFP algorithms are not suitable for continuous, unbounded and high-speed data streams mining for they need multiple database scans. In this paper, we present an efficient algorithm DSWFP, which is based on sliding window and can discover important frequent pattern from the recent data. DSWFP has three new characters, including a new refined weight definition, a new proposed data structure and two pruning strategies. Experimental studies are performed to evaluate the good effectiveness of DSWFP.