Name
Abstract | ||
|---|---|---|
Nowadays, association rules mining has become one of the predominate tasks employed to discover informative rules from large data set to support decision-making. One of the major difficulties in applying associations mining technique is the setting of an appropriate minimum support. Unfortunately, a large support threshold would hinder the discovery of some rare but informative rules. In this paper, we propose a novel algorithm called CBWon. By keeping and utilizing the set of frequent itemsets MF and an auxiliary set of infrequent α-itemsets MIFa the proposed CBWon algorithm can significantly reduce, over an order of magnitude, the computation time spent on rediscovery of frequent itemsets. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICSMC.2004.1400821 | Systems, Man and Cybernetics, 2004 IEEE International Conference |
Keywords | Field | DocType |
data mining,database management systems,decision making,CBWon,association rules mining,frequent itemsets,informative rules,large data set,support decision-making | Data mining,Computer science,Algorithm,Association rule learning,Artificial intelligence,Machine learning,Computation | Conference |
Volume | ISSN | ISBN |
4 | 1062-922X | 0-7803-8566-7 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen-Yang Lin | 1 | 399 | 35.72 |
| Ming-cheng Tseng | 2 | 73 | 6.47 |
| Ja-Hwung Su | 3 | 329 | 24.53 |