Robust Spherical Shell Clustering Using Fuzzy-Possibilistic Product Partition
Article first published online: 20 MAR 2013
© 2013 Wiley Periodicals, Inc.
International Journal of Intelligent Systems
Volume 28, Issue 6, pages 524–539, June 2013
How to Cite
Szilágyi, L. (2013), Robust Spherical Shell Clustering Using Fuzzy-Possibilistic Product Partition. Int. J. Intell. Syst., 28: 524–539. doi: 10.1002/int.21591
- Issue published online: 10 APR 2013
- Article first published online: 20 MAR 2013
One of the main challenges in the field of clustering is creating algorithms that are both accurate and robust. This paper introduces a novel fuzzy-possibilistic shell clustering model aiming at accurate detection of circles, spheres, and multidimensional spheroids in the presence of outlier data. The proposed fuzzy-possibilistic product partition c-spherical shell algorithm (FP3CSS) combines the probabilistic and possibilistic partitions in a qualitatively different way from previous, similar algorithms. The novel mixture partition is able to suppress the influence of extreme outlier data, which gives it net superiority in terms of robustness and accuracy, compared to previous algorithms.