Spectral Clustering Using Evolving Similarity Graphs

Christina Chrysouli, Anastasios Tefas

2014

Abstract

In this paper, we propose a novel spectral graph clustering method that uses evolutionary algorithms in order to optimise the structure of a graph, by using a fitness function, applied in clustering problems. Nearest neighbour graphs and variants of these graphs are used in order to form the initial population. These graphs are transformed in such a way so as to play the role of chromosomes in the evolutionary algorithm. Multiple techniques have been examined for the creation of the initial population, since it was observed that it plays an important role in the algorithm's performance. The advantage of our approach is that, although we emphasise in clustering applications, the algorithm may be applied to several other problems that can be modeled as graphs, including dimensionality reduction and classification. Experiments on traditional dance dataset and on other various multidimensional datasets were conducted using both internal and external clustering criteria as evaluation methods, which provided encouraging results.

References

  1. Bach, F. and Jordan, M. (2003). Learning spectral clustering. Technical report, UC Berkeley.
  2. CaliÁski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics Simulation and Computation, 3(1):1-27.
  3. Chapelle, O., Schölkopf, B., and Zien, A. (2006). SemiSupervised Learning. MIT Press.
  4. Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (2):224-227.
  5. De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor. University Microfilms No. 76-9381.
  6. Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of cybernetics, 4(1):95-104.
  7. He, Z., Xu, X., and Deng, S. (2005). K-anmi: A mutual information based clustering algorithm for categorical data. CoRR.
  8. Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA.
  9. Hruschka, E. R., Campello, R. J. G. B., Freitas, A. A., and De Carvalho, A. P. L. F. (2009). A survey of evolutionary algorithms for clustering. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 39(2):133-155.
  10. Iosifidis, A., Tefas, A., and Pitas, I. (2013). Minimum class variance extreme learning machine for human action recognition. Circuits and Systems for Video Technology, IEEE Transactions on, 23(11):1968-1979.
  11. Jain, A. K. (2008). Data clustering: 50 years beyond kmeans. In ECML/PKDD (1), volume 5211, pages 3-4. Springer.
  12. Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys, 31(3):264-323.
  13. Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4):395-416.
  14. Maulik, U. and Bandyopadhyay, S. (2000). Genetic algorithm-based clustering technique. Pattern Recognition, 33(9):1455-1465.
  15. Munkres, J. (1957). Algorithms for the assignment and transportation problems. Journal of the Society of Industrial and Applied Mathematics, 5(1):32-38.
  16. Murthy, C. A. and Chowdhury, N. (1996). In search of optimal clusters using genetic algorithms. Pattern Recognition Letters, 17(8):825-832.
  17. Newman, C. B. D. and Merz, C. (1998). UCI repository of machine learning databases.
  18. Ng, A. Y., Jordan, M. I., Weiss, Y., et al. (2002). On spectral clustering: Analysis and an algorithm. Advances in neural information processing systems, 2:849-856.
  19. Schaeffer, S. E. (2007). Graph clustering. Computer Science Review, 1:27-64.
  20. Vendramin, L., Campello, R. J. G. B., and Hruschka, E. R. (2009). On the comparison of relative clustering validity criteria. In SDM, pages 733-744. SIAM.
  21. Zhao, Y. and Karypis, G. (2001). Criterion functions for document clustering: Experiments and analysis.
  22. Zu Eissen, B. S. S. M. and Wißbrock, F. (2003). On cluster validity and the information need of users. ACTA Press, pages 216-221.
Download


Paper Citation


in Harvard Style

Chrysouli C. and Tefas A. (2014). Spectral Clustering Using Evolving Similarity Graphs . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 21-29. DOI: 10.5220/0005069200210029


in Bibtex Style

@conference{ecta14,
author={Christina Chrysouli and Anastasios Tefas},
title={Spectral Clustering Using Evolving Similarity Graphs},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},
year={2014},
pages={21-29},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005069200210029},
isbn={978-989-758-052-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - Spectral Clustering Using Evolving Similarity Graphs
SN - 978-989-758-052-9
AU - Chrysouli C.
AU - Tefas A.
PY - 2014
SP - 21
EP - 29
DO - 10.5220/0005069200210029