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Robust Spherical Shell Clustering Using Fuzzy‐Possibilistic Product Partition
Author(s) -
Szilágyi László
Publication year - 2013
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21591
Subject(s) - partition (number theory) , cluster analysis , outlier , robustness (evolution) , fuzzy logic , probabilistic logic , spherical shell , fuzzy clustering , computer science , data mining , mathematics , algorithm , pattern recognition (psychology) , artificial intelligence , shell (structure) , combinatorics , engineering , civil engineering , biochemistry , chemistry , gene
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 (FP 3 CSS) 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.