Title : ( A new Tree Clustering Algorithm for Fuzzy Data Based on Alphs-Cuts )
Authors: Mohammad GhasemiGol , Reza Monsefi , Hadi Sadoghi-Yazdi ,
Full TextAbstract
This paper presents a new approach to clustering fuzzy data, called Extensional Tree (ET) clustering algorithm by defining a dendrogram over fuzzy data and using a new metric between fuzzy numbers based on α-cuts. All the similar previous methods extended FCM to support fuzzy data. The present work is based on hierarchical clustering algorithm to cluster fuzzy data. In this novel approach a dendrogram is drawn over fuzzy or crisp data and then the desired clusters are extracted. Finally we compare this approach with some of the newly presented methods in the literature. The major advantage of ET is its fault tolerance against noisy samples. The overall experiments show prominence of our proposed method in comparison with other presented works.
Keywords
, Hierarchical Clustering method, Fuzzy data, Fuzzy dendrogram, Dissimilarity measure, α-cut
@inproceedings{paperid:1011702,
author = {Mohammad GhasemiGol and Monsefi, Reza and Hadi Sadoghi-Yazdi},
title = {A new Tree Clustering Algorithm for Fuzzy Data Based on Alphs-Cuts},
booktitle = {ICAI\'09 - The 2009 International Conference on Artificial Intelligence},
year = {2009},
location = {Las Vegas, Nevada, USA},
keywords = {Hierarchical Clustering method; Fuzzy data; Fuzzy dendrogram; Dissimilarity measure; α-cut},
}
%0 Conference Proceedings
%T A new Tree Clustering Algorithm for Fuzzy Data Based on Alphs-Cuts
%A Mohammad GhasemiGol
%A Monsefi, Reza
%A Hadi Sadoghi-Yazdi
%J ICAI\'09 - The 2009 International Conference on Artificial Intelligence
%D 2009
