doi: 10.4304/jsw.9.8.2080-2086
Efficient Method for Mining Patterns from Highly Similar and Dense Database based on Prefix-Frequent-Items
- 1 School of Computer and Information Technology Beijing Jiaotong University, Beijing, 100044, P.R. China
- 2 School of Computer Science and Engineering Beifang University of Nationalities, Yinchuan, 750021, P.R. China
Abstract
In recent years, there are a great deal of efforts on sequential pattern mining, but some challenges have not been resolved, such as large search spaces and the ineffectiveness in handling highly similar, dense and long sequences. This paper mainly focuses on how to design some effective search space pruning methods to accelerate the mining process. We present a novel structure, Prefix- Frequent-Items Graph (PFI-Graph), which presents the prefix frequent items of other items in sequential patterns. An efficient algorithm PFI-PrefixSpan (Prefix-Frequent- Items PrefixSpan) based on PFI-Graph is proposed in this paper. It avoids redundant data scanning, and thus can effectively speed up the discovery process of new patterns. Extensive experimental results on some synthetic and real sequence datasets show that the proposed novel structure is substantially more efficient than PrefixSpan with physicalprojection and pseudo-projection, especially for dense and highly similar sequence databases.
Keywords
- sequential
- pattern
- mining;
- dense
- databas
How to Cite
Meng Han, Zhihai Wang, and Jidong Yuan, "Efficient Method for Mining Patterns from Highly Similar and Dense Database based on Prefix-Frequent-Items," Journal of Software, vol. 9, no. 8, pp. 2080-2086, 2014. https://doi.org/ 10.4304/jsw.9.8.2080-2086
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).