Name
Abstract | ||
|---|---|---|
The data streams arising in many applications can be modeled as a union of low-dimensional subspaces known as multi-subspace data streams (MSDSs). Clustering MSDSs according to their underlying low-dimensional subspaces is a challenging problem which has not been resolved satisfactorily by existing data stream clustering (DSC) algorithms. In this paper, we propose a sparse-based DSC algorithm, which we refer to as dynamic sparse subspace clustering (D-SSC). This algorithm recovers the low-dimensional subspaces (structures) of high-dimensional data streams and finds an explicit assignment of points to subspaces in an online manner. Moreover, as an online algorithm, D-SSC is able to cope with the time-varying structure of MSDSs. The effectiveness of D-SSC is evaluated using numerical experiments. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683205 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Data stream clustering, high-dimensional data stream, subspace clustering, online clustering | Online algorithm,Subspace clustering,Data stream mining,Data stream clustering,Pattern recognition,Computer science,Linear subspace,Artificial intelligence,Cluster analysis | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinping Sui | 1 | 0 | 1.35 |
| Zhen Liu | 2 | 26 | 7.75 |
| Li Liu | 3 | 733 | 50.04 |
| Alexander Jung | 4 | 13 | 3.46 |
| Tianpeng Liu | 5 | 14 | 3.37 |
| Bo Peng | 6 | 12 | 2.38 |
| Xiang Li | 7 | 345 | 82.16 |