نتایج جستجو برای: fuzzy c means clustering

تعداد نتایج: 1530679  

2015
Teresa L. Ju Ping-Feng Pai Chih-Hung Kuo

This study proposes a novel Intuitionistic fuzzy c-least squares support vector regression (IFCLSSVR) with sammon mapping clustering algorithm. The proposed clustering algorithm can obtain the advantages of intuitionistic fuzzy sets, LSSVR, and sammon mapping in actual clustering problems. Moreover, IFC-LSSVR with sammon mapping adopts particle swarm optimization (PSO) to search optimal paramet...

2013
Parisut Jitpakdee Pakinee Aimmanee Bunyarit Uyyanonvara

Firefly algorithm is a swarm-based algorithm that can be used for solving optimization problems. In this paper, we focus on image clustering algorithm using the fuzzy set of possible solution is incorporated into the original firefly to improve the performance. The movement of the firefly still follows the original pattern but they are updated according fuzzy c-means algorithm. All method, k-me...

Journal: :Artificial intelligence in medicine 1999
Francesco Masulli Andrea Schenone

In medical imaging uncertainty is widely present in data, because of the noise in acquisition and of the partial volume effects originating from the low resolution of sensors. In particular, borders between tissues are not exactly defined and memberships in the boundary regions are intrinsically fuzzy. Therefore, computer assisted unsupervised fuzzy clustering methods turn out to be particularl...

2003
Hong Jiang

This paper extends the traditional Fuzzy C-Means clustering method to a generalized fuzzy clustering model. According to most applications, this fuzzy clustering model briefly includes 3 parts: feature extractor transfers original objects information to desired feature data; fuzzy cluster analyzer gets cluster information from the feature data; and post treatment obtains the final results based...

2004
Ameer Ali Gour C Karmakar Laurence S Dooley

Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having ...

Journal: :Pattern Recognition 1991
Mohamed S. Kamel Shokri Z. Selim

In this paper, the problem of achieving 'semi-fuzzy' or 'soft' clustering of multidimensional data is discussed.A technique based on thresholding the results of the fuzzy c-means algorithm is introduced.The proposed approach is analysed and contrasted with the soft clustering method (see S. Z. Selim and M. A. Ismail, Pattern Recognition 17, 559-568) showing the merits of the new method.Separati...

2002
S. Nascimento B. Mirkin

The Multiple Prototype Fuzzy Clustering Model (FCMP), introduced by Nascimento, Mirkin and Moura-Pires (1999), proposes a framework for partitional fuzzy clustering which suggests a model of how the data are generated from a cluster structure to be identi...ed. In the model, it is assumed that the membership of each entity to a cluster expresses a part of the cluster prototype re‡ected in the e...

2010
Ameer Ali Gour C Karmakar Laurence S Dooley

Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having ...

2013
TingZhong Wang GangLong Fan

Particle Swarm Optimization algorithm is based on iterative optimization tools, system initialization of a group of random solutions, through iterative search for the optimal value. The basic idea of the fuzzy C-means clustering algorithm is to determine each sample data belonging to a certain degree of clustering, and the degree of membership of sample data is grouped into a cluster. Favor opt...

Journal: :Pattern Recognition Letters 2007
Francisco de A. T. de Carvalho

This paper presents adaptive and non-adaptive fuzzy c-means clustering methods for partitioning symbolic interval data. The proposed methods furnish a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable squared Euclidean distances between vectors of intervals. Moreover, various cluster interpretation tools are introduced. Experiments with real an...

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