Name
Abstract | ||
|---|---|---|
This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from unknown continuous distributions, which form clusters with each cluster containing a composite set of closely located distributions (based on a certain distance metric between distributions). The maximum intracluster distance is assumed to be smaller than the minim... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSP.2019.2901370 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Clustering algorithms,Measurement,Signal processing algorithms,Error probability,Partitioning algorithms,Upper bound,Data models | Convergence (routing),Statistical physics,Cluster (physics),Data modeling,Mathematical optimization,Upper and lower bounds,Metric (mathematics),Unsupervised learning,Cluster analysis,k-medoids,Mathematics | Journal |
Volume | Issue | ISSN |
67 | 8 | 1053-587X |
Citations | PageRank | References |
2 | 0.42 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tiexing Wang | 1 | 5 | 2.57 |
| qunwei li | 2 | 68 | 6.42 |
| Donald J. Bucci | 3 | 13 | 6.09 |
| Yingbin Liang | 4 | 1646 | 147.64 |
| Biao Chen | 5 | 2258 | 199.27 |
| Pramod K. Varshney | 6 | 6689 | 594.61 |