Name
Abstract | ||
|---|---|---|
There are numerous different classification methods; among the many we can cite associative classifiers. This newly suggested model uses association rule mining to generate classification rules associating observed features with class labels. Given the binary nature of association rules, these classification models do not take into account repetition of features when categorizing. In this paper, we enhance the idea of associative classifiers with associations with re-occurring items and show that this mixture produces a good model for classification when repetition of observed features is relevant in the data mining application at hand. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11430919_30 | PAKDD |
Keywords | Field | DocType |
associative classifier,re-occurring feature,classification model,observed feature,association rule mining,good model,association rule,numerous different classification method,account repetition,classification rule,data mining application | Data mining,Associative property,Computer science,Association rule learning,Information extraction,Knowledge extraction,Artificial intelligence,Correlation and dependence,Mixture theory,Machine learning,Binary number | Conference |
Volume | ISSN | ISBN |
3518 | 0302-9743 | 3-540-26076-5 |
Citations | PageRank | References |
7 | 0.50 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafal Rak | 1 | 382 | 18.30 |
| Wojciech Stach | 2 | 387 | 16.50 |
| Osmar R. Zaïane | 3 | 3143 | 285.09 |
| Maria-Luiza Antonie | 4 | 512 | 23.94 |