Name
Abstract | ||
|---|---|---|
Clustering ensemble is a momentous technique in machine learning and contribute much to the applications in many areas. General clustering ensemble methods pay more attention to predicting cluster labels than structures of clusters. In fact, learning cluster structures implicates sufficient information to rebuild the dataset and is competent for being the replacement of redundant predicted cluster labels. In this paper, we introduce the fuzzy theory into the structure framework and propose a newfangled double fuzzy c-means structure ensemble framework, named as FCM2SE. FCM2SE makes use of the cluster structure information instead of predicted labels to gain a representative ensemble structure. We also design two novel labeling criteria to distribute the samples to the corresponding clusters. The empirical results on synthetic datasets and UCI machine learning datasets demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICMLC.2012.6359567 | ICMLC |
Keywords | Field | DocType |
fuzzy set theory,pattern clustering,clustering ensemble,fuzzy theory,learning (artificial intelligence),fuzzy c-means structure ensemble framework,fcm2se,uci machine learning datasets,learning artificial intelligence | Data mining,Cluster (physics),Fuzzy clustering,Pattern recognition,Pattern clustering,Computer science,Fuzzy logic,Fuzzy set,Artificial intelligence,Cluster analysis,Ensemble learning,Machine learning | Conference |
Volume | ISSN | ISBN |
4 | 2160-133X | 978-1-4673-1484-8 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwen Yu | 1 | 2753 | 220.67 |
| Le Li | 2 | 158 | 10.10 |
| Da-Xing Wang | 3 | 18 | 1.87 |
| Jane You | 4 | 1885 | 136.93 |
| Guoqiang Han | 5 | 439 | 43.27 |
| Hantao Chen | 6 | 36 | 2.93 |