TY - GEN
T1 - On kernelization for a maximizing model of bezdek-like spherical fuzzy c-means clustering
AU - Kanzawa, Yuchi
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - In this study, we propose three modifications for a maximizing model of spherical Bezdek-type fuzzy c-means clustering (msbFCM). First, we kernelize msbFCM (K-msbFCM). The original msbFCM can only be applied to objects on the first quadrant of the unit hypersphere, whereas its kernelized form can be applied to a wider class of objects. The second modification is a spectral clustering approach to K-msbFCM using a certain assumption. This approach solves the local convergence problem in the original algorithm. The third modification is to construct a model providing the exact solution of the spectral clustering approach. Numerical examples demonstrate that the proposed methods can produce good results for clusters with nonlinear borders when an adequate parameter value is selected.
AB - In this study, we propose three modifications for a maximizing model of spherical Bezdek-type fuzzy c-means clustering (msbFCM). First, we kernelize msbFCM (K-msbFCM). The original msbFCM can only be applied to objects on the first quadrant of the unit hypersphere, whereas its kernelized form can be applied to a wider class of objects. The second modification is a spectral clustering approach to K-msbFCM using a certain assumption. This approach solves the local convergence problem in the original algorithm. The third modification is to construct a model providing the exact solution of the spectral clustering approach. Numerical examples demonstrate that the proposed methods can produce good results for clusters with nonlinear borders when an adequate parameter value is selected.
KW - Fuzzy c-means clustering
KW - Kernelization
KW - Spectral clustering approach
UR - http://www.scopus.com/inward/record.url?scp=84911046000&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-12054-6_10
DO - 10.1007/978-3-319-12054-6_10
M3 - Conference contribution
AN - SCOPUS:84911046000
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 108
EP - 121
BT - Modeling Decisions forArtificial Intelligence - 11th International Conference, MDAI 2014, Proceedings
A2 - Torra, Vicenç
A2 - Narukawa, Yasuo
A2 - Endo, Yasunori
PB - Springer Verlag
T2 - 11th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2014
Y2 - 29 October 2014 through 31 October 2014
ER -