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A novel semi-supervised clustering algorithm for finding clusters of arbitrary shapes

Soleymani Baghshah, M ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-540-89985-3_123
  3. Publisher: 2008
  4. Abstract:
  5. Recently, several algorithms have been introduced for enhancing clustering quality by using supervision in the form of constraints. These algorithms typically utilize the pair wise constraints to either modify the clustering objective function or to learn the clustering distance measure. Very few of these algorithms show the ability of discovering clusters of different shapes along with satisfying the provided constraints. In this paper, a novel semi-supervised clustering algorithm is introduced that uses the side information and finds clusters of arbitrary shapes. This algorithm uses a two-stage clustering approach satisfying the pair wise constraints. In the first stage, the data points are grouped into a relatively large number of fuzzy ellipsoidal sub-clusters. Then, in the second stage, connections between sub-clusters are established according to the pair wise constraints and the similarity of sub-clusters. Experimental results show the ability of the proposed algorithm for finding clusters of arbitrary shapes. © 2008 Springer-Verlag
  6. Keywords:
  7. Arbitrary shape ; Clustering ; Clustering quality ; Data points ; Distance measure ; Objective functions ; Pairwise constraints ; Semi-supervised ; Semi-supervised Clustering ; Side information ; Sub-clusters ; Two-stage clustering ; Computer science ; Clustering algorithms
  8. Source: 13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008, Kish Island, 9 March 2008 through 11 March 2008 ; Volume 6 CCIS , 2008 , Pages 876-879 ; 18650929 (ISSN); 3540899847 (ISBN); 9783540899846 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-540-89985-3_123