Gaussian Mixture Models Implementation to Enhance Spectral Clustering
Abstract: Nature variability has been studied for many years due to its importance in research areas such as Biology or Medicine. In order to characterize such variability, different methods have been used. Since shape is one of the most important features of human perception, it is natural to assess the variation using shape models. As well, one of the most important activities in data analysis is clustering, meaning the task of grouping a set of objects in such a way that objects in the same group are more similar than objects in different groups. This paper presents a new shape descriptor called Angular Magnitude that is used with in a spectral clustering methodology in order to improve the clustering of complex shapes such as fractals. In addition, we propose the use of Gaussian Mixture Models as a replacement for K-means in the aforementioned methodology. Results are presented over different sets of shapes from natural and artificial objects, along with two different measurements to evaluate them quantitatively.