Name
Abstract | ||
|---|---|---|
As one of feasible clustering techniques for large-scale data, incremental fuzzy clustering, which copes with data in chunks, has triggered more attentions in recent years. The existing methods, such as online fuzzy C-medoids (OFCMd) and history-based online fuzzy C-medoids (HOFCMd), employ only one medoid to represent each cluster in chunks. Due to the fact that the representativeness of the one-medoid modality is sometimes unsatisfactory, a novel large-scale fuzzy multiple-medoid clustering (LS-FMMdC) method is presented to strengthen the clustering effectiveness for large-scale data. The performance of the proposed method is verified by comparing LS-FMMdC with OFCMd and HOFCMd on both synthetic and real-life large-scale data sets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/JIFS-152647 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Fuzzy C-medoids,incremental clustering,data chunk,multiple medoids,large-scale data | Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Correlation clustering,Pattern recognition,Fuzzy classification,Fuzzy set operations,Artificial intelligence,FLAME clustering,Cluster analysis,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 3 | 1064-1246 |
Citations | PageRank | References |
1 | 0.36 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aiguo Chen | 1 | 122 | 4.05 |
| Pengjiang Qian | 2 | 133 | 11.25 |
| Shitong Wang | 3 | 1485 | 109.13 |
| Yizhang Jiang | 4 | 382 | 27.24 |