Name
Title | ||
|---|---|---|
Utilisation d'outils de visual data mining pour l'exploration d'un ensemble de règles d'association |
Abstract | ||
|---|---|---|
Data Mining aims at extracting maximum of knowledge from huge databases. It is realized by an automatic process or by data visual exploration with interactive tools. Automatic data mining extracts all the patterns which match a set of metrics. The limit of such algorithms is the amount of extracted data which can be larger than the initial data volume. In this article, we focus on association rules extraction with Apriori algorithm. After the description of a characterization model of a set of association rules, we propose to explore the results of a Data Mining algorithm with an interactive visual tool. There are two advantages. First it will visualize the results of the algorithms from different points of view (metrics, rules attributes...). Then it allows us to select easily inside large set of rules the most relevant ones. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2044354.2044369 | IHM |
Keywords | Field | DocType |
data visual exploration,data mining,rules attribute,automatic data mining,large set,visual data mining,data mining algorithm,un ensemble,association rule,initial data volume,association rules extraction,apriori algorithm,interactive visualization,data visualization | Data mining,Information retrieval,Computer science,Apriori algorithm,Association rule learning,Visual tool,Data mining algorithm | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gwenael Bothorel | 1 | 5 | 1.57 |
| Mathieu Serrurier | 2 | 267 | 26.94 |
| Christophe Hurter | 3 | 684 | 47.30 |