Name
Abstract | ||
|---|---|---|
Traditional sequential pattern mining deals with positive sequential patterns only, that is, only frequent sequential patterns with the appearance of items are discovered. However, it is often interesting in many applications to find frequent sequential patterns with the nonoccurrence of some items, which are referred to as negative sequential patterns. This paper analyzes three types of negative sequential rules and presents a new technique to find event-oriented negative sequential rules. Its effectiveness and efficiency are shown in our experiments. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/WIIAT.2008.60 | Web Intelligence |
Keywords | Field | DocType |
efficient mining,negative sequential rule,traditional sequential pattern mining,event-oriented negative sequential rule,sequential patterns,negative sequential rules,sequence mining,data mining,new technique,event-oriented negative sequential rules,positive sequential pattern,frequent sequential pattern,sequential pattern mining,negative sequential patterns,negative sequential pattern,algorithm design and analysis,association rules,correlation | Data mining,Algorithm design,Computer science,Association rule learning,Artificial intelligence,Sequential Pattern Mining,Machine learning | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3496-1 | 9 |
PageRank | References | Authors |
0.65 | 13 | 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 |