Name
Title | ||
|---|---|---|
Scalable spectral ensemble clustering via building representative co-association matrix |
Abstract | ||
|---|---|---|
Ensemble clustering via building co-association matrix and combining multiple basic partitions from the same dataset into the consensus one has been widely used in spectral clustering and subspace clustering. However, with the ever-increasing cost of calculating the co-association matrix, the conventional ensemble clustering algorithm is no longer fit for dealing with the large-scale datasets due to its less scalability and time-consuming. In this paper, we propose a scalable spectral ensemble clustering method via building a representative co-association matrix to improve the ensemble clustering problem. Our method mainly includes constructing a sparse matrix to select the representative points and building the co-association matrix, and a robust and denoising representation for the co-association matrix can be learned through a low-rank constraint in a unified optimization framework. The experiments verify the high efficiency and scalability but less time cost of our method compared with state-of-art clustering methods in the six real-world datasets, especially in the large-scale datasets. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2020.01.055 | Neurocomputing |
Keywords | DocType | Volume |
Scalable ensemble clustering,Co-association matrix,Spectral methods,Data graph partition | Journal | 390 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinian Liang | 1 | 0 | 0.34 |
| Zhigang Ren | 2 | 21 | 3.97 |
| Zongze Wu | 3 | 65 | 11.45 |
| Deyu Zeng | 4 | 1 | 1.37 |
| Jianzhong Li | 5 | 0 | 0.34 |