Name
Abstract | ||
|---|---|---|
Association rule mining approaches traditionally generate rules based only on database contents, and focus on exact matches between items in transactions. In many applications, however, the utilization of some back- ground knowledge, such as ontologies, can enhance the discovery process and generate semantically richer rules. Besides, fuzzy logic concepts can be applied on ontologies to quantify semantic similarity relations among data. In this con- text, we extended SSDM (Semantically Similar Data Miner) algorithm in order to obtain from a fuzzy ontology the semantic relations between items. As a con- sequence, the generated rules can be more understandable, improving the utility of the knowledge supplied by them. |
Year | Venue | Keywords |
|---|---|---|
2006 | SBBD | rule based,fuzzy logic,semantic similarity,association rule mining,data mining |
Field | DocType | Citations |
Semantic similarity,Ontology (information science),Data mining,Concept mining,Information retrieval,Computer science,Fuzzy logic,Association rule learning,Business process discovery,Semantic Web Rule Language,Semantic computing,Database | Conference | 6 |
PageRank | References | Authors |
0.51 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eduardo L. G. Escovar | 1 | 11 | 1.05 |
| Cristiane A. Yaguinuma | 2 | 30 | 4.79 |
| Mauro Biajiz | 3 | 45 | 7.73 |