Fuzzy c-means clustering using Jeffreys-divergence based similarity measure

Seal, Ayan and Karlekar, Aditya and Krejcar, Ondrej and Gonzalo Martín, Consuelo (2020). Fuzzy c-means clustering using Jeffreys-divergence based similarity measure. "Applied Soft Computing", v. 88 ; pp. 106016-106027. ISSN 1568-4946. https://doi.org/10.1016/j.asoc.2019.106016.

Description

Title: Fuzzy c-means clustering using Jeffreys-divergence based similarity measure
Author/s:
  • Seal, Ayan
  • Karlekar, Aditya
  • Krejcar, Ondrej
  • Gonzalo Martín, Consuelo
Item Type: Article
Título de Revista/Publicación: Applied Soft Computing
Date: March 2020
ISSN: 1568-4946
Volume: 88
Subjects:
Freetext Keywords: Jeeffreys-divergence, Jeffreys-divergence based similarity, Measure, Fuzzy c-means, Jeffreys-fuzzy-c-means clustering
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In clustering, similarity measure has been one of the major factors for discovering the natural grouping of a given dataset by identifying hidden patterns. To determine a suitable similarity measure is an open problem in clustering analysis for several years. The purpose of this study is to make known a divergence based similarity measure. The notion of the proposed similarity measure is derived from Jeffrey-divergence. Various features of the proposed similarity measure are explained. Afterwards we develop fuzzy c-means (FCM) by making use of the proposed similarity measure, which guarantees to converge to local minima. The various characteristics of the modified FCM algorithm are also addressed. Some well known real-world and synthetic datasets are considered for the experiments. In addition to that two remote sensing image datasets are also adopted in this work to illustrate the effectiveness of the proposed FCM over some existing methods. All the obtained results demonstrate that FCM with divergence based proposed similarity measure outperforms three latest FCM algorithms.

More information

Item ID: 67984
DC Identifier: https://oa.upm.es/67984/
OAI Identifier: oai:oa.upm.es:67984
DOI: 10.1016/j.asoc.2019.106016
Official URL: https://www.sciencedirect.com/science/article/pii/S1568494619307987
Deposited by: Memoria Investigacion
Deposited on: 04 Nov 2021 07:20
Last Modified: 04 Nov 2021 07:20
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