Particle swarm clustering ensemble

ABSTRACT

Extracting natural groups of the unlabeled data is known as clustering. To improve the stability and robustness of the clustering outputs, clustering ensembles have emerged recently. In this paper, an ensemble of particle swarm clustering algorithms is proposed. That is, the members of the ensemble are based on the cooperative swarms clustering approaches. The performance of the proposed particle swarm clustering ensemble is evaluated using di®erent data sets and is compared to that of other clustering techniques.

REFERENCES

• 1: Alexander Topchy , Anil K. Jain , William Punch, Clustering Ensembles: Models of Consensus and Weak Partitions, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.27 n.12, p.1866-1881, December 2005 [doi>10.1109/TPAMI.2005.237]
• 2: A. Ahmadi, F. Karray and M. Kamel, Multiple Cooperating Swarms for Data Clustering," IEEE Proceedings of Swarm Intelligence Symposium 2007, pages 206-212.
• 3: X. Cui, T.E. Potok and P. Palathingal, Document Clustering Using Particle Swarm Optimization," IEEE Proceedings of Swarm Intelligence Symposium 2005, pages 185-191.
• 4: C.L. Blake and C.J. Merz, UCI Repository of Machine Learning Databases," 1998.
• 5: James Kennedy , Russell C. Eberhart, Swarm intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

General Terms:
Algorithms, Design

Keywords:
clustering ensemble, data clustering, multiple cooperative swarms, particle swarm optimization