Name
Abstract | ||
|---|---|---|
Mining for frequent patterns is an active research area in which numerous algorithms have been proposed to discover different types of patterns based on associative and sequential structures. However, almost all methods are designed to discover a single type of pattern rather than a user selected combination. Within this paper, a novel, flexible algorithm called S&AD is presented, which is capable of discovering a multitude of inter-field and inter-record patterns. In addition the discovery of sequential and associative patterns is supported where the methods proposed for discovery only consist of a recognition phase, thus avoiding the time expensive task of candidate generation. The structure further allows the incorporation of user-driven constraints and domain knowledge at different levels of the discovery process. |
Year | Venue | Keywords |
|---|---|---|
2003 | SEKE | sequential pattern,association,pattern discovery,pattern mining,domain knowledge |
Field | DocType | Citations |
Data mining,Associative property,Domain knowledge,Computer science,Artificial intelligence,Business process discovery,Machine learning,K-optimal pattern discovery | Conference | 1 |
PageRank | References | Authors |
0.37 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Baumgarten | 1 | 106 | 13.54 |
| Alex G. Büchner | 2 | 159 | 13.45 |
| John G. Hughes | 3 | 326 | 59.84 |