Data Stream Closed Frequent Itemsets Mining in Blend Window

Author

Hao, Wu ; Huiying, Wang ; Huaiying, Li ; Miao, Jiang

Author_Institution

Sch. of Manage., Hefei Univ. of Technol., Hefei, China

fYear

2012

fDate

11-13 Aug. 2012

Firstpage

2047

Lastpage

2051

Abstract

In data stream mining, sliding window can record the latest and most useful patterns, but the best size can not be accurately determined. To aim at data with the characteristics of data flow in some simulation systems, this paper proposes a method for mining the closed frequent patterns in the mixed window of data stream. The pattern of data stream could be completely recorded by scanning the stream only once. And the pruning method of T-Moment could reduce the space complexity of sliding window tree and the maintenance cost of the closed frequent patterns tree. To differentiate the historical and the latest patterns, a time decaying model was applied. The experimental results show that the algorithm has good efficiency and accuracy.

Keywords

computational complexity; cost reduction; data mining; tree data structures; trees (mathematics); T-Moment; blend window; closed frequent patterns tree; data flow; data stream closed frequent itemsets mining; data stream mixed window; maintenance cost reduction; pruning method; simulation systems; sliding window tree; space complexity reduction; time decaying model; Accuracy; Algorithm design and analysis; Complexity theory; Data mining; Data models; Itemsets; Vegetation; closed frequent pattern; mixed window; simulation data; time decaying;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science & Service System (CSSS), 2012 International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4673-0721-5

Type

conf

DOI

10.1109/CSSS.2012.509

Filename

6394827