doi: 10.4304/jsw.9.1.258-263
Recent Frequent Item Mining Algorithm in a Data Stream Based on Flexible Counter Windows
- 1 School of Information Engineering, Yangzhou Polytechnic College, Yangzhou,China
- 2 Department 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.
Keywords
- streaming data
- counter array
- data mini
How to Cite
Yanyang Guo, Gang Wang, Fengmei Hou, and 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. https://doi.org/ 10.4304/jsw.9.1.258-263
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).