Clustering large number of extragalactic spectra of galaxies and quasars through canopies

Tuli De, Didier Fraix Burnet and Asis Kumar Chattopadhyay

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 9, 2638-2653

Abstract: Cluster analysis is the distribution of objects into different groups or more precisely the partitioning of a data set into subsets (clusters) so that the data in subsets share some common trait according to some distance measure. Unlike classification, in clustering one has to first decide the optimum number of clusters and then assign the objects into different clusters. Solution of such problems for a large number of high dimensional data points is quite complicated and most of the existing algorithms will not perform properly. In the present work a new clustering technique applicable to large data set has been used to cluster the spectra of 702248 galaxies and quasars having 1,540 points in wavelength range imposed by the instrument. The proposed technique has successfully discovered five clusters from this 702,248X1,540 data matrix.

Date: 2016
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DOI: 10.1080/03610926.2013.848286

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