APPLICATION OF A MATHEMATICAL MORPHOLOGICAL PROCESS ON THE KOHONEN MAPS FOR UNSUPERVISED DATA CLASSIFICATION
M. Talibi-Alaoui, R. Touahni, A. Sbihi*
Laboratoire Images et Reconnaissance des Formes, LIRF
Université Ibn Tofail, FSK, B.P. 133, 14000, Kénitra, Maroc
* Corresponding author. E-mail : email@example.com
Received : 25 September 2002; revised version accepted : 09 April 2003
In this paper, we present a new data classification algorithm in an unsupervised context, which is based on both Kohonen maps and mathematical morphology.
The first part of the proposed algorithm consists to a projection of the distribution of multidimensional data observations onto a Kohonen map which is represented by the underlying probability density function (pdf). Under the assumption that each modal region of this density function has a correspondance with a one and only one cluster in the distribution, the second part of the algorithm consists in partitioning the Kohonen map into connected modal regions by making concepts of morphological watershed transformation suitable for their detection. The classification process is then based on the so detected modal regions.
Keywords : Clustering ; Mode detection ; Kohonen maps ; Mathematical Morphology.