Name
Abstract | ||
|---|---|---|
Association rule mining aims at discovering patterns whose support is beyond a given threshold. Mining patterns composed of items described by an arbitrary subset of attributes in a large relational table represents a new challenge and has various practical applications, including the event management systems that motivated this work. The attribute combinations that define the items in a pattern provide the structural information of the pattern. Current association algorithms do not make full use of the structural information of the patterns: the information is either lost after it is encoded with attribute values, or is constrained by a given hierarchy or taxonomy. Pattern structures convey important knowledge about the patterns. We present an architecture that organizes the mining space based on pattern structures. By exploiting the interrelationships among pattern structures, execution times for mining can be reduced significantly. This advantage is demonstrated by our experiments using both synthetic and real-life datasets. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICDM.2002.1183992 | ICDM |
Keywords | Field | DocType |
inter-relationshipsamong pattern structure,relational databases,mining space,pattern providethe structural information,patterns discovery,pattern structures conveyimportant knowledge,execution times formining,current associationalgorithms,arbitrary subset ofattributes,search problems,structural information,pattern structure,theattribute combination,association rule mining,data mining,event management systems,attribute,execution times,attribute value,large relational tables,mining associations,history,algorithm design and analysis,authorization,management system,association rules,data security,zinc | Data mining,Data stream mining,Concept mining,Text mining,Relational database,Computer science,Molecule mining,Association rule learning,Hierarchy,K-optimal pattern discovery | Conference |
ISBN | Citations | PageRank |
0-7695-1754-4 | 3 | 0.42 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heng Wang | 1 | 5539 | 275.36 |
| Chang-Shing Perng | 2 | 478 | 35.92 |
| Sheng Ma | 3 | 1139 | 76.32 |
| Philip S. Yu | 4 | 30670 | 3474.16 |