A Hierarchical Clustering Based Heuristic for Automatic Clustering

François LaPlante, Nabil Belacel, Mustapha Kardouchi

2014

Abstract

Determining an optimal number of clusters and producing reliable results are two challenging and critical tasks in cluster analysis. We propose a clustering method which produces valid results while automatically determining an optimal number of clusters. Our method achieves these results without user input pertaining directly to a number of clusters. The method consists of two main components: splitting and merging. In the splitting phase, a divisive hierarchical clustering method (based on the DIANA algorithm) is executed and interrupted by a heuristic function once the partial result is considered to be “adequate”. This partial result, which is likely to have too many clusters, is then fed into the merging method which merges clusters until the final optimal result is reached. Our method’s effectiveness in clustering various data sets is demonstrated, including its ability to produce valid results on data sets presenting nested or interlocking shapes. The method is compared with cluster validity analysis to other methods to which a known optimal number of clusters is provided and to other automatic clustering methods. Depending on the particularities of the data set used, our method has produced results which are roughly equivalent or better than those of the compared methods.

References

  1. Bezdek, J. C., Ehrlich, R., and Full, W. (1984). Fcm: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(23):191-203.
  2. Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2):179- 188.
  3. Fukuyama, Y. and Sugeno, M. (1989). A new method of choosing the number of clusters for the fuzzy cmeans method. In Proceedings of Fifth Fuzzy Systems Symposium, pages 247-250.
  4. Gan, G. (2011). Data Clustering in C++: An ObjectOriented Approach. Chapman and Hall/CRC.
  5. Gionis, A., Mannila, H., and Tsaparas, P. (2007). Clustering aggregation. ACM Trans. Knowl. Discov. Data, 1(1).
  6. Guan, Y., Ghorbani, A., and Belacel, N. (2003). Ymeans: a clustering method for intrusion detection. In Electrical and Computer Engineering, 2003. IEEE CCECE 2003. Canadian Conference on, volume 2, pages 1083-1086. IEEE.
  7. Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). Data clustering: A review. ACM Comput. Surv., 31(3):264- 323.
  8. Kaufman, L. R. and Rousseeuw, P. (1990). Finding groups in data: An introduction to cluster analysis.
  9. MacNaughton-Smith, P. (1964). Dissimilarity Analysis: a new Technique of Hierarchical Sub-division. Nature, 202:1034-1035.
  10. Mok, P., Huang, H., Kwok, Y., and Au, J. (2012). A robust adaptive clustering analysis method for automatic identification of clusters. Pattern Recognition, 45(8):3017-3033.
  11. Pakhira, M. K., Bandyopadhyay, S., and Maulik, U. (2004). Validity index for crisp and fuzzy clusters. Pattern Recognition, 37(3):487-501.
  12. Pal, N. and Bezdek, J. (1995). the fuzzy c-means model. Transactions on, 3(3):370-379.
  13. Rezaee, M. R., Lelieveldt, B., and Reiber, J. (1998). A new cluster validity index for the fuzzy c-mean. Pattern Recognition Letters, 19(34):237-246.
  14. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(0):53-65.
  15. Wu, K.-L. and Yang, M.-S. (2005). A cluster validity index for fuzzy clustering. Pattern Recognition Letters, 26(9):1275-1291.
  16. Xie, X. and Beni, G. (1991). A validity measure for fuzzy clustering. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 13(8):841-847.
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Paper Citation


in Harvard Style

LaPlante F., Belacel N. and Kardouchi M. (2014). A Hierarchical Clustering Based Heuristic for Automatic Clustering . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 201-210. DOI: 10.5220/0004925902010210


in Bibtex Style

@conference{icaart14,
author={François LaPlante and Nabil Belacel and Mustapha Kardouchi},
title={A Hierarchical Clustering Based Heuristic for Automatic Clustering},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={201-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004925902010210},
isbn={978-989-758-015-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A Hierarchical Clustering Based Heuristic for Automatic Clustering
SN - 978-989-758-015-4
AU - LaPlante F.
AU - Belacel N.
AU - Kardouchi M.
PY - 2014
SP - 201
EP - 210
DO - 10.5220/0004925902010210