Name
Abstract | ||
|---|---|---|
We present a hybrid heuristic algorithm, clusterAOI, that generates a more interesting generalised table than obtained via attribute-oriented induction (AOI). AOI tends to overgeneralise as it uses a fixed global static threshold to cluster and generalise attributes irrespective of their features, and does not evaluate intermediate interestingness. In contrast, clusterAOI uses attribute features to dynamically recalculate new attribute thresholds and applies heuristics to evaluate cluster quality and intermediate interestingness. Experimental results show improved interestingness, better output pattern distribution and expressiveness, and improved runtime. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.dss.2013.08.012 | Decision Support Systems |
Keywords | Field | DocType |
recalculate new attribute threshold,cluster quality,attribute-oriented mining,improved runtime,intermediate interestingness,clusteraoi use,better output pattern distribution,hybrid heuristic algorithm,attribute-oriented induction,fixed global static threshold,hybrid heuristic approach | Data mining,Heuristic,Heuristic (computer science),Computer science,Heuristics,Artificial intelligence,Machine learning,Expressivity | Journal |
Volume | ISSN | Citations |
57, | 0167-9236 | 4 |
PageRank | References | Authors |
0.44 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maybin K. Muyeba | 1 | 47 | 7.61 |
| Keeley Crockett | 2 | 541 | 30.27 |
| Wenjia Wang | 3 | 57 | 9.12 |
| John A. Keane | 4 | 695 | 92.81 |