Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological opera

Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each object with hundreds of associated parameters. Exploration of this very high-dimensional data space poses a huge challenge. Subspace clustering is one among several approaches which have been proposed for this purpose in recent years. However, many clustering algorithms require the user to set a large number of parameters without any guidelines. Some methods also do not provide a concise summary of the datasets, or, if they do, they lack additional important information such as the number of clusters present or the significance of the clusters. In this paper, we propose a method for ranking subspaces for clustering which overcomes many of the above limitations. First we carry out a transformation from parametric space to discrete image space where the data are represented by a grid-based density field. Then we ...

• Type: Journal
• Year: 2010
• Where: IEEEVAST
• Authors: Bilkis J. Ferdosi, Hugo Buddelmeijer, Scott Trager, Michael H. F. Wilkinson, Jos B. T. M. Roerdink
• Keywords: Astronomical, Density Field, Emerging Technology, IEEEVAST 2010, Modern Astronomical Surveys