Cluster Analysis Based on Mathematical Morphology
Luo Hui, Kong Fan · 2006
ABSTRACT Mathematical morphology has been widely used in image analysis. Classical clustering methods often fail to deliver satisfactory results, especially when clusters have arbitrary shapes. Through some techniques for selecting discretization parameters and structural elements, a new approach to cluster analysis is proposed, which is based on the mathematical, morphology operations. Clusters are well separated by means of hierarchical mathematical morphology procedures. Experimental results demonstrate that the proposed clustering algorithm clusters data better than the classical clustering algorithms, and an optimal number of clusters could be found.