NDLI: Cluster analysis of massive datasets in astronomy

Content Provider	Springer Nature Link
Author	Jang, Woncheol Hendry, Martin
Copyright Year	2007
Abstract	Clusters of galaxies are a useful proxy to trace the distribution of mass in the universe. By measuring the mass of clusters of galaxies on different scales, one can follow the evolution of the mass distribution (Martínez and Saar, Statistics of the Galaxy Distribution, 2002). It can be shown that finding galaxy clusters is equivalent to finding density contour clusters (Hartigan, Clustering Algorithms, 1975): connected components of the level set S $_{ c }$≡{f>c} where f is a probability density function. Cuevas et al. (Can. J. Stat. 28, 367–382, 2000; Comput. Stat. Data Anal. 36, 441–459, 2001) proposed a nonparametric method for density contour clusters, attempting to find density contour clusters by the minimal spanning tree. While their algorithm is conceptually simple, it requires intensive computations for large datasets. We propose a more efficient clustering method based on their algorithm with the Fast Fourier Transform (FFT). The method is applied to a study of galaxy clustering on large astronomical sky survey data.
Starting Page	253
Ending Page	262
Page Count	10
File Format	PDF
ISSN	09603174
Journal	Statistics and Computing
Volume Number	17
Issue Number	3
e-ISSN	15731375
Language	English
Publisher	Springer US
Publisher Date	2007-07-27
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Density contour cluster Level set Clustering Fast Fourier transform Artificial Intelligence (incl. Robotics) Mathematical Modeling and Industrial Mathematics Numeric Computing Statistics Statistics and Computing/Statistics Programs
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Theoretical Computer Science Computational Theory and Mathematics Statistics, Probability and Uncertainty

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