Results 211 to 220 of about 15,044 (262)

Suppressed fuzzy c-means clustering algorithm

Pattern Recognition Letters, 2003
Summary: Based on the defect of rival checked fuzzy \(c\)-means clustering algorithm, a new algorithm: suppressed fuzzy \(c\)-means clustering algorithm is proposed. The new algorithm overcomes the shortcomings of the original algorithm, establishes more natural and more reasonable relationships between hard \(c\)-means clustering algorithm and fuzzy \(
Jiu-Lun Fan 0001, Wen-Zhi Zhen, Weixin Xie   +2 more
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Gaussian Collaborative Fuzzy C-Means Clustering

International Journal of Fuzzy Systems, 2021
For most FCM-based fuzzy clustering algorithms, several problems, such as noise, non-spherical clusters, and size-imbalanced clusters, are difficult to solve. Different fuzzy clustering algorithms are developed to deal with these problems from different perspectives. However, no comprehensive viewpoint to generalize these problems has been put forward.
Yunlong Gao 0001, Zhihao Wang, Huidui Li, Jinyan Pan   +3 more
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A fuzzy clustering model of data and fuzzy c-means

Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2002
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 identified.
Susana Nascimento, Boris G. Mirkin, Fernando Moura-Pires   +2 more
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Fuzzy C-means and fuzzy swarm for fuzzy clustering problem

Expert Systems with Applications, 2011
Fuzzy clustering is an important problem which is the subject of active research in several real-world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement.
Hesam Izakian, Ajith Abraham
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Fuzzy approaches to hard c-means clustering

2012 IEEE International Conference on Fuzzy Systems, 2012
A popular clustering model is hard c-means (HCM). For many data sets the HCM objective function has local extrema, so HCM optimization often yields suboptimal clusterings. The effect of local extrema can be reduced by fuzzification, leading to the well-known fuzzy c-means (FCM) model with the fuzziness parameter m > 1.
Thomas A. Runkler, James M. Keller
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Fuzzy c-means for Fuzzy Hierarchical Clustering

The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05., 2005
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.
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An improved fuzzy C-means clustering algorithm

2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016
Sensitive to the initial number and centers of clusters is one shortcoming of fuzzy c-means clustering method. Aiming to reduce the sensitivity, a partial supervision-based fuzzy c-means clustering method is proposed in this paper. In this method, the data is first clustered with standard fuzzy c-means algorithm. If the clustering result doesn't accord
Lingzi Duan, Fusheng Yu, Li Zhan
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Projected Rough Fuzzy c-means clustering

2011 11th International Conference on Intelligent Systems Design and Applications, 2011
The conventional rough set based feature selection techniques find the relevant features for the entire data set. However different sets of dimensions may be relevant for different clusters. This paper introduces a novel Projected Rough Fuzzy c-means clustering algorithm (PRFCM) which employs rough sets to model uncertainty in data, and fuzzy set ...
Charu Pun, Naveen Kumar 0001
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Soil clustering by fuzzy c-means algorithm

Advances in Engineering Software, 2005
In this study, hard k-means and fuzzy c-means algorithms are utilized for the classification of fine grained soils in terms of shear strength and plasticity index parameters. In order to collect data, several laboratory tests are performed on 120 undisturbed soil samples, which are obtained from Antalya region.
A. Burak Göktepe, Selim Altun, Alper Sezer   +2 more
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An efficient Fuzzy C-Means clustering algorithm

Proceedings 2001 IEEE International Conference on Data Mining, 2002
The Fuzzy C-Means (FCM) algorithm is commonly used for clustering. The performance of the FCM algorithm depends on the selection of the initial cluster center and/or the initial membership value. If a good initial cluster center that is close to the actual final cluster center can be found, the FCM algorithm will converge very quickly and the ...
Ming-Chuan Hung, Don-Lin Yang
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