Name
Abstract | ||
|---|---|---|
Semi-supervised clustering is a promising approach for improving partition quality of unsupervised clustering in large-scale data analysis while it is often difficult to utilize an enough amount of supervised objects. A virtual sample approach is a practical technique for improving classification quality in semi-supervised learning, in which additional virtual samples are generated by combining several supervised objects. In this research, the virtual sample approach is adopted in semi-supervised fuzzy co-clustering and its characteristics are demonstrated through a numerical experiment. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TAAI.2015.7407057 | 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI) |
Keywords | Field | DocType |
semi-supervised fuzzy co-clustering,virtual sample approach,semi-supervised learning,classification quality,supervised object,large-scale data analysis,unsupervised clustering,partition quality,semi-supervised clustering,partial supervision | Data mining,Computer science,Fuzzy logic,Artificial intelligence,Biclustering,Cluster analysis,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tanaka, D. | 1 | 3 | 1.59 |
| Katsuhiro Honda | 2 | 289 | 63.11 |
| Seiki Ubukata | 3 | 19 | 18.99 |
| Akira Notsu | 4 | 146 | 42.93 |