Name
Abstract | ||
|---|---|---|
Discovering temporal patterns from sequence data has been an important task of data mining in recent years. In this paper a novel temporal pattern, Intervention , is proposed to capture the partial ordering relations in parallel event sequences. It is demonstrated that Intervention is essentially a deviation of generalized Markov property holding in parallel event sequences. A measure to evaluate the degree of such deviation, Intervention Intensity , is suggested, which has an important mathematical property, non-symmetry. As a result, an algorithm called MIPES for mining interventions is proposed. The time complexity of MIPES is of O (m 2) and is independent of data size, where m is the number of event types and is far smaller than the data size in practice. The experimental results show MIPES is applicable and scalable. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-00672-2_27 | APWeb/WAIM |
Keywords | Field | DocType |
important task,data mining,mining interventions,event type,parallel event sequence,generalized markov property,sequence data,parallel event sequences,mining intervention,important mathematical property,data size,intervention intensity,partial order,time complexity,markov property,intervention | Data mining,Psychological intervention,Markov property,Computer science,Data sequences,Time complexity,Partially ordered set,Scalability | Conference |
Volume | ISSN | Citations |
5446 | 0302-9743 | 2 |
PageRank | References | Authors |
0.41 | 14 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Yang | 1 | 29 | 6.90 |
| Changjie Tang | 2 | 483 | 62.75 |
| Yue Wang | 3 | 18 | 6.63 |
| Rong Tang | 4 | 4 | 0.78 |
| Chuan Li | 5 | 49 | 5.32 |
| Jiaoling Zheng | 6 | 2 | 1.42 |
| Jun Zhu | 7 | 26 | 13.25 |