Robust Spherical Shell Clustering Using Fuzzy-Possibilistic Product Partition

Authors

  • László Szilágyi

    Corresponding author
    • Sapientia—Hungarian University of Transylvania, Faculty of Technical and Human Sciences, Tîrgu-Mureş, Romania
    Search for more papers by this author

Author to whom all correspondence should be addressed: e-mail: lalo@ms.sapientia.ro.

Abstract

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.

Ancillary