Integration of distributed biological data using modified k-means algorithm

Abstract

The goals of bioinformatics are the solving of biological questions and the active driving of the work of biologists by offering search and analysis methods for research data. The internet brings us distributed environments in which we can access the databases of various research groups. However, a very large quantity of data always causes trouble, creating crucial problems, such as problems with the search for and analysis of data in these distributed environments. Data clustering can be a solution when searching for data. However, this task is very tedious because its execution time is directly proportional to the volume of data. In this paper we propose a distributed clustering scenario and a modified K-means algorithm for the efficient clustering of biological data, and demonstrate the enhancement in performance that it brings. © Springer-Verlag Berlin Heidelberg 2007.