Volume 9 Number 1 (Jan. 2014)
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JSW 2014 Vol.9(1): 258-263 ISSN: 1796-217X
doi: 10.4304/jsw.9.1.258-263

Recent Frequent Item Mining Algorithm in a Data Stream Based on Flexible Counter Windows

Yanyang Guo1, Gang Wang1, Fengmei Hou1, Qingling Mei2
1School of Information Engineering, Yangzhou Polytechnic College, Yangzhou,China
2Department of Computer Science, Yangzhou University, Yangzhou,China

Abstract—In the paper the author introduces FCW_MRFI, which is a streaming data frequent item mining algorithm based on variable window. The FCW_MRFI algorithm can mine frequent item in any window of recent streaming data, whose given length is L. Meanwhile, it divides recent streaming data into several windows of variable length according to m, which is the number of the counter array. This algorithm can achieve smaller query error in recent windows, and can minimize the maximum query error in the whole recent streaming data.

Index Terms—streaming data, counter array, data mining, most recent frequent item


Cite: Yanyang Guo, Gang Wang, Fengmei Hou, Qingling Mei, "Recent Frequent Item Mining Algorithm in a Data Stream Based on Flexible Counter Windows," Journal of Software vol. 9, no. 1, pp. 258-263, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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