Name
Abstract | ||
|---|---|---|
This paper describes the components of a human-centered process for discovering association rules where the user is considered as a heuristic which drives the mining algorithms via a well-adapted interface. In this approach, inspired by experimental works on behaviors during a discovery stage, the rule extraction is dynamic : at each step, the user can focus on a subset of potentially interesting items and launch an algorithm for extracting the relevant associated rules according to statistical measures. The discovered rules are represented by a graph updated at each step, and the mining algorithm is an adaptation of the well-known A Priori algorithm where rules are computed locally. Experimental results on a real corpus built from marketing data illustrate the different steps of this process. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-45372-5_55 | PKDD |
Keywords | Field | DocType |
discovery stage,mining algorithm,interesting item,experimental work,marketing data,well-known a priori algorithm,mining association rules,association rule,user-driven process,different step,human-centered process | Data mining,Knowledge representation and reasoning,Heuristic,Computer science,Apriori algorithm,Association rule learning,Information extraction,Artificial intelligence,Knowledge extraction,User interface,Machine learning,Knowledge acquisition | Conference |
Volume | ISSN | ISBN |
1910 | 0302-9743 | 3-540-41066-X |
Citations | PageRank | References |
12 | 0.75 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pascale Kuntz | 1 | 326 | 40.63 |
| Fabrice Guillet | 2 | 364 | 62.24 |
| Rémi Lehn | 3 | 28 | 4.18 |
| Henri Briand | 4 | 364 | 70.43 |