Comparison Among Clustering and Classification Techniques on the Basis of Galaxy Data

Abstract

Abtsrcat Clustering and classification of difierent astronomical objects have become one of the most important area in the field of Astrostatistics. The basic objective of cluster analysis is related to segmentation of a collection of objects into a number, may be unknown, of clusters such that objects in the same cluster are more closely related than those assigned to different clusters. Various methods are available for clustering, which may be broadly categorized under supervised and unsupervised learning. In case of supervised learning there are some input variables, called predictors and also some output variables, called responses. But in case of unsupervised learning only predictors are under consideration in the absence of responses. Under both the above mentioned categories, for clustering and classification, several methods have been developed on the basis of the underlying nature of data sets. However, there is no well known criteria to compare the performances of difierent techniques. The present paper is an attempt to compare the appli- cability of some of the clustering techniques on the basis of Gaussian and non Gaussian astronomical data sets. A post classification technique is used as a supervised learning to justify the robustness of the variety of unsupervised methods used in this purpose. Finally the similarity of clusters, obtained from different methods, is viewed in terms of astrophysical properties of the objects grouped in different clusters.