Name
Abstract | ||
|---|---|---|
Abstract: Computing frequent itemsets and their frequencies from large boolean matrices (e.g., to derive association rules) has been one of the hot topics in data mining. Levelwise algorithms (e.g., the APRIORI algorithm) have been proved effective for frequent itemset 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 item-sets. The last three years, two promising issues have been investigated: the use of user defined constraints and closed sets mining. To the best of our knowledge, combining these two frameworks has not been studied yet. In this paper, we show that the benefit of these two approaches can be combined into levelwise algorithms. An experimental validation related to the discovery of association rules with negations is reported. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/IDEAS.2001.938100 | IDEAS |
Keywords | Field | DocType |
closed sets mining,data mining,levelwise algorithm,sparse data,apriori algorithm,frequent itemset mining,experimental validation,frequent item-sets,association rule,mining free itemsets,computing frequent itemsets,database theory,sparse matrices,computability,a priori algorithm,negations,computer applications,association rules,frequency,testing,transaction processing,prototypes | Transaction processing,Data mining,Computer science,Apriori algorithm,Computability,Closed set,Association rule learning,Database theory,Sparse matrix,Database,Computation | Conference |
ISBN | Citations | PageRank |
0-7695-1140-6 | 30 | 1.61 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean-Francois Boulicaut | 1 | 365 | 54.85 |
| baptiste jeudy | 2 | 98 | 8.44 |