Name
Abstract | ||
|---|---|---|
Mining association rules at multiple concept levels leads to the discovery of more concrete knowledge. Nevertheless, setting an appropriate minsup for cross-level itemsets is still a non-trivial task. Besides, the mining process is computationally expensive and produces many redundant rules. In this work, we propose an efficient algorithm for mining generalized association rules with multiple minsup. The method scans appropriately k+1 times of the number of original transactions and once of the extended transactions where k is the largest itemset size. Encouraging simulation results were reported. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-45372-5_59 | PKDD |
Keywords | Field | DocType |
mining process,cross-level itemsets,generalized association rules,multiple concept level,concrete knowledge,multiple minimum supports,appropriate minsup,mining association rule,extended transaction,efficient algorithm,generalized association rule,multiple minsup,association rule | Data mining,Computer science,Concept learning,Association rule learning,Knowledge engineering,Artificial intelligence,Knowledge acquisition | Conference |
Volume | ISSN | ISBN |
1910 | 0302-9743 | 3-540-41066-X |
Citations | PageRank | References |
19 | 0.92 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chung-Leung Lui | 1 | 22 | 1.34 |
| Fu Lai Chung | 2 | 1534 | 86.72 |