Results 291 to 300 of about 66,224 (345)

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
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Evolutionary fuzzy c-means clustering algorithm

Proceedings of 1995 IEEE International Conference on Fuzzy Systems. The International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, 2002
In this paper, a new approach to fuzzy clustering is introduced. This approach, which is based on the application of an evolutionary strategy to the fuzzy c-means clustering algorithm, utilizes the relationship between the various definitions of distance and structures implied in each given data set.
null Bo Yuan, G.J. Klir, J.F. Swan-Stone
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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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From Soft Clustering to Hard Clustering: A Collaborative Annealing Fuzzy $c$-Means Algorithm

IEEE transactions on fuzzy systems
The fuzzy c-means clustering algorithm is the most widely used soft clustering algorithm. In contrast to hard clustering, the cluster membership of data generated using the fuzzy c-means algorithm is ambiguous.
Hongzong Li, Jun Wang
semanticscholar   +1 more source

Fuzzy C-Means clustering through SSIM and patch for image segmentation

Applied Soft Computing, 2020
In this study, we propose a new robust Fuzzy C-Means (FCM) algorithm for image segmentation called the patch-based fuzzy local similarity c-means (PFLSCM).
Yiming Tang, Fuji Ren, W. Pedrycz
semanticscholar   +1 more source

Local segmentation of images using an improved fuzzy C-means clustering algorithm based on self-adaptive dictionary learning

Applied Soft Computing, 2020
Image segmentation is an active research topic in image processing. The Fuzzy C-means (FCM) clustering analysis has been widely used in image segmentation. As there is a large amount of delicate tissues such as blood vessels and nerves in medical images,
Jiaqing Miao, Xiaobing Zhou, Tingzhu Huang   +2 more
semanticscholar   +1 more source

Cluster Validity for the Fuzzy c-Means Clustering Algorithrm

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982
The uniform data function is a function which assigns to the output of the fuzzy c-means (Fc-M) or fuzzy isodata algorithm a number which measures the quality or validity of the clustering produced by the algorithm. For the preselected number of cluster c, the Fc-M algorithm produces c vectors in the space in which the data lie, called cluster centers,
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Ensemble clustering via Fuzzy c-Means

2017 International Conference on Service Systems and Service Management, 2017
Ensemble clustering is to fuse several basic partitions to find a single best cluster structure of data. With the prevalence of heterogeneous data rising from various application domains, ensemble clustering has become a state-of-the-art solution for cluster analysis due to its robustness and generalizability.
null Xin Wan, null Hao Lin, null Hong Li, null Guannan Liu, null Maobo An   +4 more
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Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering

2015
The choice of initial center plays a great role in achieving optimal clustering results in all partitional clustering approaches. Fuzzy C-means is a widely used approach but it also gets trapped in local optima values due to sensitiveness to initial cluster centers.
P. Gopala Krishna, D. Lalitha Bhaskari
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A weighted fuzzy c-means clustering model for fuzzy data

Computational Statistics & Data Analysis, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
D'URSO, Pierpaolo, GIORDANI, Paolo
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