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Feature-based data stream clustering

Jafari Asbagh, M ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1109/ICIS.2009.172
  3. Publisher: 2009
  4. Abstract:
  5. Data stream clustering has attracted a huge attention in recent years. Many one-pass and evolving algorithms have been developed in this field but feature selection and its influence on clustering solution has not been addressed by these algorithms. In this paper we explain a feature-based clustering method for streaming data. Our method establishes a ranking between features based on their appropriateness in terms of clustering compactness and separateness. Then, it uses an automatic algorithm to identify unimportant features and remove them from feature set. These two steps take place continuously during lifetime of clustering task. © 2009 IEEE
  6. Keywords:
  7. Automatic algorithms ; Clustering methods ; Clustering solutions ; Data stream ; Data stream clustering ; Feature selection ; Feature sets ; Feature-based ; One-pass ; One-pass clustering ; Streaming data ; Data communication systems ; Information science ; Clustering algorithms
  8. Source: Proceedings of the 2009 8th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2009, 1 June 2009 through 3 June 2009, Shanghai ; 2009 , Pages 363-368 ; 9780769536415 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/5222900