Quasar Candidate Selection by Clustering using Fibonacci Series for Astronomical Surveys
It is becoming apparent that the next generation astronomical analysis requires good domain knowledge to handle vast amount of astronomical data over network. The data availability over internet has increased tremendously and there exist a bridging gap between computer scientists and astronomers. Most of the conventional methods are directly applied to the data without any simplification mechanism. The k-means is a clustering algorithm that has been used widely to classify dataset in astronomical databases. In this paper, the authors propose an approach to group Quasar candidates with redshirts varying between 0.8 and 2.2 from SDSS (Sloan Digital Sky Survey) catalogue.