Comparing Model-based Versus K-means Clustering for the Planar Shapes

In some fields, there is an interest in distinguishing different geometrical objects from each other. A field of research that studies the objects from a statistical point of view, provided they are invariant under translation, rotation and scaling effects, is known as the statistical shape analysis. Having some objects that are registered using key points on the outline of the objects, the main purpose of this paper is to compare two popular clustering procedures to cluster objects. We also use some indexes to evaluate our clustering application. The proposed methods are applied to the real life data.