A Fuzzy Clustering Model for Fuzzy Data with Outliers
Computer Vision and Pattern Recognition
2015-03-17 v1
Abstract
In this paper a fuzzy clustering model for fuzzy data with outliers is proposed. The model is based on Wasserstein distance between interval valued data which is generalized to fuzzy data. In addition, Keller's approach is used to identify outliers and reduce their influences. We have also defined a transformation to change our distance to the Euclidean distance. With the help of this approach, the problem of fuzzy clustering of fuzzy data is reduced to fuzzy clustering of crisp data. In order to show the performance of the proposed clustering algorithm, two simulation experiments are discussed.
Keywords
Cite
@article{arxiv.1011.4321,
title = {A Fuzzy Clustering Model for Fuzzy Data with Outliers},
author = {M. H. Fazel Zarandi and Zahra S. Razaee},
journal= {arXiv preprint arXiv:1011.4321},
year = {2015}
}
Comments
18 pages, Journal paper