Sequential Patterns Mining Scaling with Data Stream Based on LSP-tree

Author

Huang, Qinhua ; Ouyang, Weimin

Author_Institution

Modern Educ. Technol. Center, Shanghai Univ. of Political Sci. & Law, Shanghai, China

Volume

5

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

614

Lastpage

618

Abstract

We present a new method of mining sequential patterns in data stream based on a fast bitmap method. In recent years data stream emerges as a new data type in many applications. When processing data stream, the memory is fixed, new stream elements flow continuously. The stream data can not be paused or completely stored. We developed a LSP-tree data structure to store the discovered sequential patterns. To be suitable for the time-changing stream data, a time-tilted window is applied to scale with continuously increased LSP-tree. Experiments on a set of large data stream demonstrate the utility of this algorithm.

Keywords

data mining; trees (mathematics); LSP-tree; data stream; fast bitmap method; sequential pattern mining; Application software; Clustering algorithms; Computer networks; Data mining; Data structures; Educational technology; Fuzzy systems; Tail; Telecommunication traffic; Unsolicited electronic mail; data mining; data stream; sequential patterns; time-tilted window;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3735-1

Type

conf

DOI

10.1109/FSKD.2009.54

Filename

5360548