Sparse Subspace Clustering for Evolving Data Streams
| Autor: | Alexander Jung, Li Liu, Zhen Liu, Tianpeng Liu, Bo Peng, Xiang Li, Jinping Sui |
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| Rok vydání: | 2019 |
| Předmět: |
ta113
Data stream clustering business.industry Computer science Data stream mining Structure (category theory) Pattern recognition 02 engineering and technology Linear subspace subspace clustering Subspace clustering online clustering 020204 information systems high-dimensional data stream 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Online algorithm business Cluster analysis |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp.2019.8683205 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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