Unsupervised Clustering on Astrophysics Data: Asteroids Reflectance Spectra Surveys and Hyperspectral Images

Details Unsupervised Clustering on Astrophysics Data: Asteroids Reflectance Spectra Surveys and Hyperspectral Images Unsupervised Clustering on Astrophysics Data: Asteroids Reflectance Spectra Surveys and Hyperspectral Images

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008aipc.1082..165g&link_type=abstract

CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: ``Classification and Di

Computer Science

Databases

Astronomical Catalogs, Atlases, Sky Surveys, Databases, Retrieval Systems, Archives, Etc., Asteroids, Meteoroids, Image Processing

Scientific paper

We present an original approach to cluster multi-component datasets, in the frame of two applications: taxonomic classification of asteroids and clustering of chemical species on a Mars hyperspectral image. The method is based on an estimation of the number of clusters and an initialization of clusters centroids in order to partition the data with a popular clustering algorithm: K-means. These information are extracted from the construction of a minimal spanning tree (MST) and under the assumption that the vertices are approximately distributed according to a Poisson distribution. We also introduce some recents populars clustering algorithms based on spectral graph theory: spectral clustering methods. Metrics used for measuring similarity between multidimensional data points are based on symmetrical divergences (e.g Kullback-Leibler and RO˝nyi). The use of these informational divergences together with the proposed method leads to better results than some other clustering methods applied to astrophysical data such as: popular reflectance spectra surveys such as Eight Color Asteroid Survey and Small Main Belt Asteroid Spectroscopic Survey II, and hyper-spectral images.

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