Mining frequent pattern based on fading factor in data streams

Author

Ren, Jia-dong ; He, Hui-ling ; Hu, Chang-zhen ; Xu, Li-na ; Wang, Li-bo

Author_Institution

Coll. of Inf. Sci. & Eng., Yanshan Univ., Qinhuangdao, China

Volume

4

fYear

2009

fDate

12-15 July 2009

Firstpage

2250

Lastpage

2254

Abstract

In order to improve the mining efficiency of frequent patterns in data streams, we present an algorithm DS-FPM for mining frequent patterns in data streams. First, a data structure DSFP-tree is constructed and the data stream is divided into a set of segments, then potential frequent itemsets on each segment are obtained by IGFA algorithm, while the generated itemsets and the remaining itemsets of DSFP-tree generated by the earlier segment and sampled by fading factor are stored in new DSFP-tree, finally, the frequent patterns in the data stream can be rapidly found by a breadth-first search strategy. The experimental result shows that the execution efficiency of DS-FPM is better than that of FPIL-stream algorithm.

Keywords

data mining; tree data structures; DS-FPM algorithm; FPIL-stream algorithm; breadth-first search strategy; data streams; data structure; fading factor; pattern mining; Computer science; Cybernetics; Data engineering; Data mining; Data structures; Educational institutions; Fading; Information science; Itemsets; Machine learning; Data streams; Fading factor; Frequent pattern;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2009 International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3702-3

Electronic_ISBN

978-1-4244-3703-0

Type

conf

DOI

10.1109/ICMLC.2009.5212115

Filename

5212115