Hierarchical tree clustering of fuzzy number

Authors:
Hadi Sadoghi Yazdi;Mohammad GhasemiGol;Sohrab Effati;Azam Jiriani;Reza Monsefi
Affiliations:
Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran and Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, ...;Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;Faculty of Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran and Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Ir ...;Faculty of Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran;Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Year:
2014

Citing 7
Cited 0

On a class of fuzzy c-numbers clustering procedures for fuzzy data

Fuzzy Sets and Systems
Metrics and orders in space of fuzzy numbers

Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Robust fuzzy clustering with fuzzy data

AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
A parametric model for fusing heterogeneous fuzzy data

IEEE Transactions on Fuzzy Systems
Hierarchical unsupervised fuzzy clustering

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new hierarchical tree approach to clustering fuzzy data, namely extensional tree ET clustering algorithm. It defines a dendrogram over fuzzy data and using a new distance between fuzzy numbers based on α-cuts. The present work is based on hierarchical clustering algorithm unlike existing methods which improve FCM to support fuzzy data. The Proposed ET clustering algorithm is compared with some of the newly presented methods in the literature. The major advantage of ET, first tree clustering method over fuzzy number, in comparison with other algorithms is its fault tolerance against noisy samples. Some examples prove ability of the proposed ET.