Name
Abstract | ||
|---|---|---|
In data mining and machine learning, the definition of the distance between two data points substantially affects clustering and classification tasks. We propose a distance metric learning (DML) method for multi-label clustering, that uses evolutionary multi-objective optimization and a cluster validity measure with a neighbor relation that simultaneously evaluates inter- and intra-clusters. The proposed method produces clustering results considering multiple class labels and allows the induction of knowledge regarding relations between class labels in multi-label clustering or between objective functions and elements in transform matrix. Experimental results have shown that the proposed DML method produces better transform matrices than single-objective optimization and is helpful in finding the attributes that affect the trade-off relationship among objective functions. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | distance metric learning, semi-supervised clustering, Mahalanobis distance, multi-label, multi-objective optimization |
Field | DocType | Citations |
Fuzzy clustering,Computer science,Consensus clustering,Artificial intelligence,Cluster analysis,Single-linkage clustering,k-medians clustering,Hierarchical clustering,Mathematical optimization,Pattern recognition,Correlation clustering,Constrained clustering,Machine learning | Conference | 3 |
PageRank | References | Authors |
0.39 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taishi Megano | 1 | 8 | 1.19 |
| Ken-ichi Fukui | 2 | 24 | 9.74 |
| Masayuki Numao | 3 | 390 | 89.56 |
| Satoshi Ono | 4 | 219 | 39.83 |