Title :
DSWFP: Efficient mining of weighted frequent pattern over data streams
Author :
Jie Wang ; Yu Zeng
Author_Institution :
Sch. of Manage., Capital Normal Univ., Beijing, China
Abstract :
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.
Keywords :
data mining; pattern recognition; DSWFP; WFP mining; data streams; sliding window; weighted frequent pattern; Algorithm design and analysis; Conferences; Data mining; Itemsets; Registers; Scalability; DSWFP; Sliding window; data mining; data streams; weighted frequent pattern mining;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
DOI :
10.1109/FSKD.2011.6019763
