DSWFP: Efficient Mining of Weighted Frequent Pattern over Data Streams.

International Conference on Computer Science and Application Engineering（2012）

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摘要

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.

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关键词

data mining,data structures,optimisation,DS-CWFP algorithm,DS-CWFP data structure,closed weighted frequent pattern mining,data streams,frequent pattern discovery,item weight,optimization strategies,sliding window,Algorithm optimization,DS_CWFP,Sliding window,closed weighted frequent pattern mining,data mining,data streams

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