Name
Abstract | ||
|---|---|---|
Association mining often produces large collections of association rules that are difficult to understand and put into action. In this paper, we have designed a novel notion of combined patterns to extract useful and actionable knowledge from a large amount of learned rules. We also present definitions of combined patterns, design novel metrics to measure their interestingness and analyze the redundancy in combined patterns. Experimental results on real-life social security data demonstrate the effectiveness and potential of the proposed approach in extracting actionable knowledge from complex data. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-89378-3_40 | Australasian Conference on Artificial Intelligence |
Keywords | Field | DocType |
actionable knowledge,novel notion,learned rules,real-life social security data,complex data,large amount,association rule,large collection,combined pattern,combined pattern mining,design novel metrics,association mining | Data science,Computer science,Complex data type,Association mining,Redundancy (engineering),Association rule learning | Conference |
Volume | ISSN | Citations |
5360 | 0302-9743 | 8 |
PageRank | References | Authors |
0.59 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanchang Zhao | 1 | 233 | 20.01 |
| Huaifeng Zhang | 2 | 240 | 18.84 |
| Longbing Cao | 3 | 2212 | 185.04 |
| Chengqi Zhang | 4 | 3636 | 274.41 |
| Hans Bohlscheid | 5 | 40 | 3.71 |