Name
Abstract | ||
|---|---|---|
The association rule discovery problem consists in identif ying frequent itemsets in a database and, then, forming conditional implication rule s among them. The algorithmically most difficult part of this task is finding all frequent sets. There exists a wealth of algorithms both for the problem as such and for variations, particular c ases, and generalizations. Except for some recent, fully different approaches, most algorith ms can be seen either as a breadth- first search or a depth-first search of the lattice of itemsets. In this paper, we propose a way of developing best-first search strategies. |
Year | Venue | Keywords |
|---|---|---|
2002 | EGC | depth-first search. mots-clés : data mining,breadt h-first search,frequent itemsets,data mining,itemsets fréquents,qu ête en largeur d'abord,association rules,quête en profondeur d'abord.,règles d'association,breadth first search,depth first search,association rule |
Field | DocType | Citations |
Data mining,Combinatorics,Association rule discovery,Computer science | Conference | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaume Baixeries | 1 | 99 | 12.57 |
| Gemma Casas-garriga | 2 | 46 | 3.79 |
| José L. Balcázar | 3 | 701 | 62.06 |