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 A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 554 ◽  
Author(s):  
Barbara Cardone ◽  
Ferdinando Di Martino
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Fuzzy Entropy ◽  
Entropy Function ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Random Initialization ◽  
Fuzzy C Means Algorithm ◽  
Optimal Cluster

One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. In this paper we propose a variation of FCM in which the initial optimal cluster centers are obtained by implementing a weighted FCM algorithm in which the weights are assigned by calculating a Shannon Fuzzy Entropy function. The results of the comparison tests applied on various classification datasets of the UCI Machine Learning Repository show that our algorithm improved in all cases relating to the performances of FCM.

scholarly journals Grouping Districts/Cities in Kalimantan Island Based on The People's Welfare Indicators Using Fuzzy C-Means and Subtractive Fuzzy C-Means Methods

2021 ◽  
Vol 18 (1) ◽  
pp. 141-149
Author(s):  
Nur Annisa Fitri ◽  
Memi Nor Hayati ◽  
Rito Goejantoro
Keyword(s):  
Cluster Analysis ◽  
Welfare Indicators ◽  
Fuzzy C Means ◽  
Welfare Indicator ◽  
Grouping Method ◽  
Indicator Data ◽  
Optimal Cluster

Cluster analysis has the aim of grouping several objects of observation based on the data found in the information to describe the objects and their relationships. The grouping method used in this research is the Fuzzy C-Means (FCM) and Subtractive Fuzzy C-Means (SFCM) methods. The two grouping methods were applied to the people's welfare indicator data in 42 regencies/cities on the island of Kalimantan. The purpose of this study was to obtain the results of grouping districts/cities on the island of Kalimantan based on indicators of people's welfare and to obtain the results of a comparison of the FCM and SFCM methods. Based on the results of the analysis, the FCM and SFCM methods yield the same conclusions, so that in this study the FCM and SFCM methods are both good to use in classifying districts/cities on the island of Kalimantan based on people's welfare indicators and produce an optimal cluster of two clusters, namely the first cluster consisting of 10 Regencies/Cities on the island of Kalimantan, while the second cluster consists of 32 districts/cities on the island of Borneo.

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.

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
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