Cluster Analysis Using Improved Bio-inspired Fuzzy C-Means Algorithm

2021 ◽  
pp. 99-111
Author(s):  
Fisokuhle Mthethwa ◽  
Amitabha Nath
Keyword(s):  
Cluster Analysis ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

A Modified Fuzzy C-Mean Algorithm for Automatic Clustering Number

2013 ◽  
Vol 333-335 ◽  
pp. 1418-1421
Author(s):  
Cheng Quan Huang
Keyword(s):  
Cluster Analysis ◽  
Significant Role ◽  
Real Life ◽  
Theory And Practice ◽  
Fuzzy Membership ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Automatic Clustering ◽  
Fuzzy C Means Algorithm

FCM(Fuzzy C-Means) algorithm is an important algorithm in cluster analysis. It plays an significant role in theory and practice. However, the clustering number of FCM algorithm needs to be set beforehand. This paper proposes an automatic clustering number determination for the classical FCM(Fuzzy C-Means) algorithm. The proposed automatic clustering number determination is based on the cardinality of clustering fuzzy membership used in the CA(Competitive Agglomeration) algorithm. The effectiveness of the proposed algorithm, along with a comparison with CA algorithm, has been showed both qualitatively and quantitatively on a set of real-life datasets.

Hybrid chemical reaction based metaheuristic with fuzzy c-means algorithm for optimal cluster analysis

2017 ◽  
Vol 79 ◽  
pp. 282-295 ◽  
Author(s):  
Janmenjoy Nayak ◽  
Bighnaraj Naik ◽  
Himansu Sekhar Behera ◽  
Ajith Abraham
Keyword(s):  
Cluster Analysis ◽  
Chemical Reaction ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm ◽  
Optimal Cluster

scholarly journals Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 696
Author(s):  
Haipeng Chen ◽  
Zeyu Xie ◽  
Yongping Huang ◽  
Di Gai
Keyword(s):  
Brain Tumor ◽  
Information Transfer ◽  
Magnetic Resonance Images ◽  
Low Noise ◽  
Similarity Measurement ◽  
Noise Sensitivity ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Membership Information ◽  
Fuzzy C Means Algorithm

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.

Landcover classification of satellite images based on an adaptive interval fuzzy c-means algorithm coupled with spatial information

2019 ◽  
Vol 41 (6) ◽  
pp. 2189-2208
Author(s):  
Jindong Xu ◽  
Guozheng Feng ◽  
Baode Fan ◽  
Weiqing Yan ◽  
Tianyu Zhao ◽  
...  
Keyword(s):  
Satellite Images ◽  
Spatial Information ◽  
Fuzzy C Means ◽  
Landcover Classification ◽  
Fuzzy C Means Algorithm

scholarly journals A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

2011 ◽  
Vol 07 (01) ◽  
pp. 155-171 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Segmentation ◽  
Point Of View ◽  
Neutrosophic Set ◽  
Convergence Criterion ◽  
Complex Objects ◽  
Fuzzy C Means ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

Research of Lithium-Ion Battery Sorting Method Based on Fuzzy C-Means Algorithm

2011 ◽  
Vol 354-355 ◽  
pp. 983-988 ◽  
Author(s):  
Lei Guo ◽  
Gen Wang Liu
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Test System ◽  
Cluster Number ◽  
Sorting Method ◽  
Fuzzy C Means ◽  
Weighted Index ◽  
Fuzzy C Means Algorithm

A method that applies Fuzzy C-Means algorithm (FCM) in lithium-ion battery sorting in formation and test system is presented. The original Fuzzy C-Means algorithm is optimized, and weighted index m and cluster number c are analyzed. Through battery sorting experiment, this method is verified to be feasible and practical.

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