Summary
International Symposium on Nonlinear Theory and its Applications
2010
Session Number:C3L-C
Session:
Number:C3L-C1
Hard and Fuzzy c-Means Clustering Algorithms with Geodesic Dissimilarity
Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto,
pp.603-606
Publication Date:2010/9/5
Online ISSN:2188-5079
Summary:
In this paper, the geodesic distance is applied to relational clustering methods. First, it is shown that conventionalmethods are based on respective three types of relational clustering algorithms among nine ones, and the six rests of the nine ones with the geodesic distance are proposed. Second, geodesic dissimilarity is proposed by assigning the power of the Euclidean distance to the weight of the neighborhood graph of data. Numerical examples show that the proposed geodesic-dissimilarity-based relational clustering algorithms successfully cluster the data that conventional squared-Euclidean-distance-based ones cannot.