# REAL TIME CLUSTERING MODEL

### J. Cheng, M. R. Sayeh, M. R. Zargham

#### Abstract

This paper focuses on the development of a dynamic system model in unsupervised learning environment. This adaptive dynamic system consists of a set of energy functions which create valleys for representing clusters. Each valley represents a cluster of similar input patterns. The system includes a dynamic parameter for the clustering vigilance so that the cluster size or the quantizing resolution can be adaptive to the density of the input patterns. It also includes a factor for invoking competitive exclusion among the valleys; forcing only one label to be assigned to each cluster. Through several examples of different pattern clusters, it is shown that the model can successfully cluster these types of input patterns and form different sizes of clusters according to the size of the input patterns.

#### References

- Block, H.-H., 2002. Clustering Methods: From Classical Models to New Approaches, Statistics in Transition, Vol. 5, No. 5, October 2002, pp. 725-758.
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- MacQueen, J. B. 1967. Some methods for classification and analysis of multivariate observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press. Berkeley, 1:281-297.
- Cheng, J., Sayeh, M.R., & Zargham, M.R. 2006. Neural Net Based Models for Clustering. International Journal of Computational Intelligence Theory and Practice, 1(2), 91-102.

#### Paper Citation

#### in Harvard Style

Cheng J., R. Sayeh M. and R. Zargham M. (2008). **REAL TIME CLUSTERING MODEL** . In *Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,* ISBN 978-989-8111-37-1, pages 235-240. DOI: 10.5220/0001694002350240

#### in Bibtex Style

@conference{iceis08,

author={J. Cheng and M. R. Sayeh and M. R. Zargham},

title={REAL TIME CLUSTERING MODEL},

booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

year={2008},

pages={235-240},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0001694002350240},

isbn={978-989-8111-37-1},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,

TI - REAL TIME CLUSTERING MODEL

SN - 978-989-8111-37-1

AU - Cheng J.

AU - R. Sayeh M.

AU - R. Zargham M.

PY - 2008

SP - 235

EP - 240

DO - 10.5220/0001694002350240