Name
Abstract | ||
|---|---|---|
A novel unsupervised clustering algorithm called Hyperclique Pattern-KMEANS (HP-KMEANS) is presented. Considering recent success in semi-supervised clustering using pair-wise constraints, an unsupervised clustering method that selects constraints automatically based on Hyperclique patterns is proposed. The COP-KMEANS framework is then adopted to cluster instances of data sets into corresponding groups. Experiments demonstrate promising results compared to classical unsupervised k-means clustering. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761252 | 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 |
Keywords | Field | DocType |
k means clustering,algorithm design and analysis,clustering algorithms,unsupervised learning,machine learning,data mining | Canopy clustering algorithm,Fuzzy clustering,Data mining,CURE data clustering algorithm,Pattern recognition,Correlation clustering,Computer science,Unsupervised learning,Artificial intelligence,Conceptual clustering,Cluster analysis,Single-linkage clustering | Conference |
ISSN | Citations | PageRank |
1051-4651 | 2 | 0.36 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuchou Chang | 1 | 194 | 15.86 |
| Dah-Jye Lee | 2 | 422 | 42.05 |
| James K. Archibald | 3 | 632 | 161.01 |
| Yi Hong | 4 | 2 | 0.36 |