Name
Title | ||
|---|---|---|
Mining Both Positive and Negative Impact-Oriented Sequential Rules from Transactional Data |
Abstract | ||
|---|---|---|
Traditional sequential pattern mining deals with positive correlation between sequential patterns only, without considering negative relationship between them. In this paper, we present a notion of impact-oriented negative sequential rules , in which the left side is a positive sequential pattern or its negation, and the right side is a predefined outcome or its negation. Impact-oriented negative sequential rules are formally defined to show the impact of sequential patterns on the outcome, and an efficient algorithm is designed to discover both positive and negative impact-oriented sequential rules. Experimental results on both synthetic data and real-life data show the efficiency and effectiveness of the proposed technique. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-01307-2_65 | PAKDD |
Keywords | Field | DocType |
sequential pattern,real-life data,left side,transactional data,positive sequential pattern,positive correlation,predefined outcome,negative relationship,impact-oriented negative sequential rule,negative impact-oriented sequential rules,traditional sequential pattern mining,negative impact-oriented sequential rule,transaction data,synthetic data,sequential pattern mining | Negative relationship,Data mining,Negation,Computer science,Synthetic data,Positive correlation,Artificial intelligence,Sequential Pattern Mining,Transaction data,Machine learning | Conference |
Volume | ISSN | Citations |
5476 | 0302-9743 | 11 |
PageRank | References | Authors |
0.75 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanchang Zhao | 1 | 233 | 20.01 |
| Huaifeng Zhang | 2 | 240 | 18.84 |
| Longbing Cao | 3 | 2212 | 185.04 |
| Chengqi Zhang | 4 | 3636 | 274.41 |
| Hans Bohlscheid | 5 | 40 | 3.71 |