The fall point identification of cluster warhead based on fuzzy C-Means clustering method

2015 ◽  
Author(s):  
G.L. Wang ◽  
S.Q. Dong ◽  
X.L. Shen ◽  
J.R. Lu
Keyword(s):  
Clustering Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

New fuzzy C-means clustering method based on feature-weight and cluster-weight learning

2019 ◽  
Vol 78 ◽  
pp. 324-345 ◽  
Author(s):  
Mahdi Hashemzadeh ◽  
Amin Golzari Oskouei ◽  
Nacer Farajzadeh
Keyword(s):  
Clustering Method ◽  
Fuzzy C Means ◽  
Feature Weight ◽  
Fuzzy C Means Clustering ◽  
Weight Learning

scholarly journals Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Tran Dinh Khang ◽  
Nguyen Duc Vuong ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Clustering Method ◽  
Machine Learning Technique ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Learning Technique ◽  
Fuzzy C Means Clustering

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

scholarly journals Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

Computation ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 54 ◽  
Author(s):  
Anbu ◽  
Thangavelu ◽  
Ashok
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Rule ◽  
Discrete Wavelet ◽  
Clustering Method ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

Mapping Soil Texture of a Plain Area Using Fuzzy-c-Means Clustering Method Based on Land Surface Diurnal Temperature Difference

Pedosphere ◽  
2012 ◽  
Vol 22 (3) ◽  
pp. 394-403 ◽  
Author(s):  
De-Cai WANG ◽  
Gan-Lin ZHANG ◽  
Xian-Zhang PAN ◽  
Yu-Guo ZHAO ◽  
Ming-Song ZHAO ◽  
...  
Keyword(s):  
Temperature Difference ◽  
Soil Texture ◽  
Land Surface ◽  
Clustering Method ◽  
Diurnal Temperature ◽  
Fuzzy C Means ◽  
Plain Area ◽  
Fuzzy C Means Clustering

Comparison of classical and clustering based reference interval calculations for Aspartate Aminotransferase, Alanine Aminotransferase and Gamma-Glutamyl Transferase in serum / Aspartat Aminotransferaz, Alanin Aminotransferaz ve Gama Glutamil Transferaz serum için klasik ve kümelemeye dayalı referans aralık hesaplamalarının karşılaştırılması

2015 ◽  
Vol 40 (5) ◽  
Author(s):  
Erdal Coşgun ◽  
Deniz İlhan Topçu ◽  
Yahya Laleli
Keyword(s):  
Aspartate Aminotransferase ◽  
Alanine Aminotransferase ◽  
Reference Interval ◽  
Reference Population ◽  
Reference Intervals ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Glutamyl Transferase ◽  
Non Parametric

AbstractObjective: There are fixed rules applied to determine the reference intervals (RIs) of the biochemical tests. However, these rules lack for identifying subgroups within the reference population. Therefore, we suggest the clustering method, which determines the sub-groups by taking the correlations between the variables into account in the RIs calculations. In our study, it is aimed to compare the results of the RIs based on the clusters analysis with the results of the conventional method.Methods: The individuals who applied Ankara Düzen Laboratory for the check-up with normal Ultra Sono Grafi (USG) in 2012-2014 and who have had Aspartate Aminotransferase (AST), Alanine Aminotransferase (ALT) and G-Glutamyl Transferase (GGT); (U/L) results were included in this study. We have excluded the repeated applies of patients, only analyzed the first apply to the laboratory. Reference individuals are composed of 883 people. (610 male, 273 female, 18-70 years). Non-parametric methods were used to determine reference intervals. Fuzzy C-Means clustering method was used to identify sub-groups.Results: AST, ALT, GGT measurements for all of the check-up individuals were determined by non-parametric method for the three subgroups specified after the Fuzzy- C-Means clustering method and the entire group. According to the reference intervals obtained, the third sub-cluster derived from the group intended to be used as the reference population was observed as a cluster that is narrower, and has similar properties of the actual reference population. However, when the correlations between the tests in the sub-groups are considered, the correlations between GGT-ALT-AST have been observed to be higher while the correlation level between ALT-AST in the group proposed as a real reference population does not change.Conclusion: In the reference limit studies, instead of the determination of the reference interval for a single group designated as the reference population, we think that, the subgroups which are homogeneous within itself, heterogeneous between themselves should be set in in this group. In determining multiple sub-groups, the relationship between more than one test need to be taken into consideration, and the effect of clustering should be investigated.

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