Table of Contents
ISRN Computational Mathematics
Volume 2013, Article ID 931019, 8 pages
Research Article

Evolutionary Algorithms for Robust Density-Based Data Clustering

School of Science, Engineering and Technology, Penn State Harrisburg, Middletown, PA 17057, USA

Received 28 November 2012; Accepted 17 December 2012

Academic Editors: R. A. Krohling and R. Pandey

Copyright © 2013 Amit Banerjee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Density-based clustering methods are known to be robust against outliers in data; however, they are sensitive to user-specified parameters, the selection of which is not trivial. Moreover, relational data clustering is an area that has received considerably less attention than object data clustering. In this paper, two approaches to robust density-based clustering for relational data using evolutionary computation are investigated.