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Book ChapterDOI

Introduction to Fuzzy Clustering

About: The article was published on 2006-01-01. It has received 27 citations till now. The article focuses on the topics: Fuzzy clustering & Fuzzy set operations.
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Journal ArticleDOI
TL;DR: A new semi-supervised fuzzy clustering algorithm that uses an iterative strategy from the formulae of solutions is designed and revealed that the new algorithm has better clustering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method namedSSFC-SC.
Abstract: Dental X-ray image segmentation has an important role in practical dentistry and is widely used in the discovery of odontological diseases, tooth archeology and in automated dental identification systems. Enhancing the accuracy of dental segmentation is the main focus of researchers, involving various machine learning methods to be applied in order to gain the best performance. However, most of the currently used methods are facing problems of threshold, curve functions, choosing suitable parameters and detecting common boundaries among clusters. In this paper, we will present a new semi-supervised fuzzy clustering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmentation problem. Firstly, features of a dental X-Ray image are modeled into a spatial objective function, which are then to be integrated into a new semi-supervised fuzzy clustering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzy-stochastic environment, is applied to get the cluster centers and the membership matrix of the model. Thirdly, theoretically validation of the solutions including the convergence rate, bounds of parameters, and the comparison with solutions of other relevant methods is performed. Lastly, a new semi-supervised fuzzy clustering algorithm that uses an iterative strategy from the formulae of solutions is designed. This new algorithm was experimentally validated and compared with the relevant ones in terms of clustering quality on a real dataset including 56 dental X-ray images in the period 2014–2015 of Hanoi Medial University, Vietnam. The results revealed that the new algorithm has better clustering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method named SSFC-SC. We also suggest the most appropriate values of parameters for the new algorithm.

64 citations


Cites background from "Introduction to Fuzzy Clustering"

  • ...But again, it meets challenges in choosing parameters and detecting common boundaries among clusters [4, 21, 22, 33]....

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Proceedings ArticleDOI
25 May 2005
TL;DR: An algorithm for building fuzzy hierarchies where the elements can have fuzzy membership to the nodes is described, and the functions needed to operate with fuzzy variables are described.
Abstract: This paper describes an algorithm for building fuzzy hierarchies. These are hierarchies where the elements can have fuzzy membership to the nodes. The paper presents an approach that mainly follows a bottom-up strategy, and describes the functions needed to operate with fuzzy variables. An example of the application of the approach is also presented

62 citations

Proceedings ArticleDOI
01 Jun 2008
TL;DR: This paper introduced a method to define intuitionistic fuzzy partitions from the results of fuzzy clustering, and extended the previous results with other types of fuzzy clustersering algorithms.
Abstract: Motivated by our research on specific information loss measures (in privacy preserving data mining) and our need to compare fuzzy clusters, we proposed in a recent paper a definition for intuitionistic fuzzy partitions. We showed how to define them in the framework of fuzzy clustering. That is, we introduced a method to define intuitionistic fuzzy partitions from the results of fuzzy clustering. In this paper we further study such intuitionistic fuzzy partitions and we extend our previous results with other types of fuzzy clustering algorithms.

18 citations


Cites methods or result from "Introduction to Fuzzy Clustering"

  • ...In this paper we further study such intuitionistic fuzzy partitions and we extend our previous results with other types of fuzzy clustering algorithms....

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  • ...In order to permit the application of our method to fuzzy clusters obtained by e.g. fuzzy c-means with different parameters m, we have revised a previous definition of intuitionistic fuzzy partition....

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Book ChapterDOI
04 Nov 2007
TL;DR: In this study, Particle Swarm Optimization and Rough Set Theory are used for setting the most suitable values of the collaboration links between the data sites, and it is concluded that the overall effect of the Collaborative clustering has been improved.
Abstract: Revealing the common underlying structure of data spread across multiple data sites by applying clustering techniques is the aim of collaborative clustering, a recent and innovative idea brought up on the basis of exchanging information granules instead of data patterns. The strength of the collaboration between each pair of data repositories is determined by a user-driven parameter, both in vertical and horizontal collaborative fuzzy clustering. In this study, Particle Swarm Optimization and Rough Set Theory are used for setting the most suitable values of the collaboration links between the data sites. Encouraging empirical results uncovered the deep impact observed at the individual clusters, allowing us to conclude that the overall effect of the collaboration has been improved.

14 citations


Cites background from "Introduction to Fuzzy Clustering"

  • ...A milestone in this field was achieved with the introduction of fuzzy clustering [2], allowing the representation of many real-life situations where patterns are to be classified in more than one subset....

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Dissertation
10 Oct 2014
TL;DR: Tese de doutoramento em Programa Doutoral em Sistemas de Transportes apresentada a Faculdade de Ciencias e Tecnologia da Universidade de Coimbra as discussed by the authors
Abstract: Tese de doutoramento em Programa Doutoral em Sistemas de Transportes apresentada a Faculdade de Ciencias e Tecnologia da Universidade de Coimbra.

13 citations