Name
Abstract | ||
|---|---|---|
Association rule mining is a popular data mining task. It has an interactive and iterative nature, i.e., the user has to refine his mining queries until he is satisfied with the discovered patterns. To support such an interactive process, we propose to optimize sequences of queries by means of a cache that stores information from previous queries. Unlike related works, we use condensed representations like free and closed itemsets for both data mining and caching. This results in a much more efficient mining technique in highly correlated data and a much smaller cache than in previous approaches. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-45681-3_19 | PKDD |
Keywords | Field | DocType |
data mining,mining query,popular data mining task,previous query,correlated data,condensed representations,interactive process,association rule mining,interactive association rule mining,smaller cache,efficient mining technique,previous approach,association rule,satisfiability | Data mining,Data stream mining,Concept mining,Cache,Computer science,CPU cache,Molecule mining,Association rule learning,Information extraction,Artificial intelligence,Knowledge extraction,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-44037-2 | 18 | 0.81 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| baptiste jeudy | 1 | 98 | 8.44 |
| Jean-Francois Boulicaut | 2 | 365 | 54.85 |