Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
On the accuracy of binned kernel density estimators
Journal of Multivariate Analysis
Very fast EM-based mixture model clustering using multiresolution kd-trees
Proceedings of the 1998 conference on Advances in neural information processing systems II
Cluster analysis: a further approach based on density estimation
Computational Statistics & Data Analysis
Clustering Algorithms
MiniMax Methods for Image Reconstruction
MiniMax Methods for Image Reconstruction
A Classification Framework for Anomaly Detection
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A breadth-first approach to memory-efficient graph search
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Nonparametric density estimation and clustering in astronomical sky surveys
Computational Statistics & Data Analysis
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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 驴{fc} 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.