Frequent Items Mining on Data Stream Based on Weighted Counts

Author

Guo, Yanyang ; Jiang, Zhaoyin ; Wang, Yuan Yuan ; Mei, Qingling

Author_Institution

Sch. of Inf. Eng., Yangzhou Polytech. Coll., Yangzhou, China

fYear

2011

fDate

10-12 Oct. 2011

Firstpage

48

Lastpage

54

Abstract

Frequent items mining is an important data mining task with many real-world applications. By considering different weights of the items, weighted frequent items mining can discover more important knowledge compared to traditional frequent patterns mining. In this paper, we presented a new algorithm called count-MH to discover weighted frequent items over data streams, the proposed method is based on weighted factor and hash function where its space complexity is O(e/ε)ln(-M/(ln p) + h/(s - ε)), the processing time for each item is O(1) in average. Experimental results show that count-MH is efficient for frequent items mining.

Keywords

computational complexity; data mining; file organisation; data mining task; data streams; frequent items mining; frequent patterns mining; hash function; space complexity; weighted counts; weighted factor; weighted frequent items; Distributed computing; Hash function; data mining; data stream; frequent items; weighted counts;

fLanguage

English

Publisher

ieee

Conference_Titel

Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2011 International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4577-1827-4

Type

conf

DOI

10.1109/CyberC.2011.17

Filename

6079401