Name
Abstract | ||
|---|---|---|
In several applications, sequence databases generally update incrementally with time. Obviously, it is impractical and inefficient to re-mine sequential patterns from scratch every time a number of new sequences are added into the database. Some recent studies have focused on mining sequential patterns in an incremental manner; however, most of them only considered patterns extracted from time point-based data. In this paper, we proposed an efficient algorithm, Inc_TPMiner, to incrementally mine sequential patterns from interval-based data. We also employ some optimization techniques to reduce the search space effectively. The experimental results indicate that Inc_TPMiner is efficient in execution time and possesses scalability. Finally, we show the practicability of incremental mining of interval-based sequential patterns on real datasets. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/DSAA.2014.7058089 | DSAA |
Keywords | DocType | Citations |
optimisation,inc_tpminer,interval-based database,incremental mining,sequential patterns mining,sequence database,dynamic representation,data mining,sequential pattern mining,interval-based pattern,optimization technique,optimization,algorithm design and analysis,databases,silicon | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi-Cheng Chen | 1 | 187 | 12.72 |
| Tzu-Ya Weng | 2 | 20 | 5.40 |
| Jun-Zhe Wang | 3 | 35 | 2.82 |
| Chien-Li Chou | 4 | 86 | 10.09 |
| Jiun-Long Huang | 5 | 592 | 47.09 |
| Suh-Yin Lee | 6 | 1596 | 319.67 |