Name
Abstract | ||
|---|---|---|
Levelwise algorithms (e.g., the APRIORI algorithm) have been proved effective for association rule mining from sparse data. However, in many practical applications, the computation turns to be intractable for the user-given frequency threshold and the lack of focus leads to huge collections of frequent itemsets. To tackle these problems, two promising issues have been investigated during the last four years: the efficient use of user defined constraints and the computation of condensed representations for frequent itemsets, e.g., the frequent closed sets. We show that the benefits of these two approaches can be combined into a levelwise algorithm. It can be used for the discovery of association rules in difficult cases (dense and highly-correlated data). For instance, we report an experimental validation related to the discovery of association rules with negations. |
Year | Venue | Keywords |
|---|---|---|
2002 | Intell. Data Anal. | highly-correlated data,levelwise algorithm,frequent itemsets,sparse data,condensed representation,apriori algorithm,frequent closed set,difficult case,association rule mining,association rule,association rule mining query,association rules |
Field | DocType | Volume |
Data mining,Computer science,Apriori algorithm,Closed set,Association rule learning,Artificial intelligence,K-optimal pattern discovery,Sparse matrix,Machine learning,Computation | Journal | 6 |
Issue | Citations | PageRank |
4 | 30 | 1.50 |
References | Authors | |
21 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| baptiste jeudy | 1 | 98 | 8.44 |
| Jean-François Boulicaut | 2 | 1162 | 102.38 |