doi: 10.4304/jsw.9.9.2342-2347
An Algorithm of Top-<i>k</i> High Utility Itemsets Mining over Data Stream
- 1 School of Software, Nanyang Institute of Technology, Nanyang, Henan 473000, China
- 2 School of Innovation and Experiment, Dalian University of Technology, Liaoning, China 116024
- 3 School of Information Engineering, Ningbo Dahongying University, Ningbo, Zhejiang, China 315175
Abstract
Existing top-k high utility itemset (HUI) mining algorithms generate candidate itemsets in the mining process; their time & space performance might be severely affected when the dataset is large or contains many long transactions; and when applied to data streams, the performance of corresponding mining algorithm is especially crucial. To address this issue, propose a sliding window based top-k HUIs mining algorithm TOPK-SW; it first stores each batch data of current window as well as the items’ utility information to a tree called HUI-Tree, which ensures effective retrieval of utility values without re-scan the dataset, so as to efficiently improve the mining performance. TOPK-SW was tested on 4 classical datasets; results show that TOPK-SW outperforms existing algorithms significantly in both time and space efficiency, especially the time performance improves over 1 order of magnitude.
Keywords
- data stream
- high utility itemset
- frequ
How to Cite
Tianjun Lu, Yang Liu, and Le Wang, "An Algorithm of Top-<i>k</i> High Utility Itemsets Mining over Data Stream," Journal of Software, vol. 9, no. 9, pp. 2342-2347, 2014. https://doi.org/ 10.4304/jsw.9.9.2342-2347
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).