Abstract
In this paper, we study the issues related to the design and construction of high-performance sequence index structures in large sequence databases. To build effective indices, a novel method, called SeqIndex, is proposed, in which the selection of indices is based on the analysis of discriminative, frequent sequential patterns mined from large sequence databases. Such an analysis leads to the construction of compact and effective indexing structures. Furthermore, we eliminate the requirement of setting an optimal support threshold beforehand, which is difficult for users to provide in practice. The discriminative, frequent pattern based indexing method is proven very effective based on our performance study.
| Original language | English (US) |
|---|---|
| Pages | 601-605 |
| Number of pages | 5 |
| State | Published - Dec 1 2005 |
| Event | 5th SIAM International Conference on Data Mining, SDM 2005 - Newport Beach, CA, United States Duration: Apr 21 2005 → Apr 23 2005 |
Other
| Other | 5th SIAM International Conference on Data Mining, SDM 2005 |
|---|---|
| Country | United States |
| City | Newport Beach, CA |
| Period | 4/21/05 → 4/23/05 |
ASJC Scopus subject areas
- Software
Cite this
Cheng, H., Yan, X., & Han, J. (2005). SeqIndex: Indexing sequences by sequential pattern analysis. 601-605. Paper presented at 5th SIAM International Conference on Data Mining, SDM 2005, Newport Beach, CA, United States.