Clustering large number of extragalactic spectra of galaxies and quasars
  through canopies
  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 classi cation, in clustering one has to rst 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 1540
points in wavelength range imposed by the instrument. The proposed technique
has successfully discovered ve clusters from this 702248X1540 data matrix.
