Name
Abstract | ||
|---|---|---|
Nonnegative Matrix Factorization (NMF) is an effective tool for clustering nonnegative data, either for computing a flat partitioning of a dataset or for determining a hierarchy of similarity. In this paper, we propose a parallel algorithm for hierarchical clustering that uses a divide-and-conquer approach based on rank-two NMF to split a data set into two cohesive parts. Not only does this approa... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/HiPC50609.2020.00028 | 2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC) |
Keywords | DocType | ISSN |
Program processors,Scalability,Clustering algorithms,Bandwidth,Tools,Approximation algorithms,Classification algorithms | Conference | 1094-7256 |
ISBN | Citations | PageRank |
978-1-6654-2292-5 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lawton Manning | 1 | 0 | 0.34 |
| Grey Ballard | 2 | 503 | 32.73 |
| Ramakrishnan Kannan | 3 | 133 | 18.57 |
| Haesun Park | 4 | 3546 | 232.42 |