scholarly journals Transformer condition assessment using fuzzy C-means clustering techniques

2019 ◽  
Vol 35 (2) ◽  
pp. 47-55 ◽  
Author(s):  
Samuel Eke ◽  
Guy Clerc ◽  
Thomas Aka-Ngnui ◽  
I. Fofana
Keyword(s):  
Condition Assessment ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Fuzzy Clustering

2017 ◽  
pp. 40-61 ◽  
Author(s):  
Mashhour H. Baeshen ◽  
Malcolm J. Beynon ◽  
Kate L. Daunt
Keyword(s):  
Data Analysis ◽  
Service Quality ◽  
Mobile Phone ◽  
Fuzzy Clustering ◽  
Data Set ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Fuzzy Environment ◽  
External Variables ◽  
Fuzzy C Means Clustering

This chapter presents a study of the development of the clustering methodology to data analysis, with particular attention to the analysis from a crisp environment to a fuzzy environment. An applied problem concerning service quality (using SERVQUAL) of mobile phone users, and subsequent loyalty and satisfaction forms the data set to demonstrate the clustering issue. Following details on both the crisp k-means and fuzzy c-means clustering techniques, comparable results from their analysis are shown, on a subset of data, to enable both graphical and statistical elucidation. Fuzzy c-means is then employed on the full SERVQUAL dimensions, and the established results interpreted before tested on external variables, namely the level of loyalty and satisfaction across the different clusters established.

scholarly journals FUZZY C-MEANS IN FINDING SUBTYPES OF CANCERS IN CANCER DATABASE

2014 ◽  
Vol 07 (01) ◽  
pp. 1450018 ◽  
Author(s):  
S. R. KANNAN ◽  
S. RAMTHILAGAM ◽  
R. DEVI ◽  
T. P. HONG
Keyword(s):  
Breast Cancer ◽  
Distance Measure ◽  
Error Matrix ◽  
Weighted Distance ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Time Series Dataset ◽  
Yeast Dataset ◽  
Fuzzy C Means Clustering ◽  
Renyi’S Entropy

Finding subtypes of cancer in breast cancer database is an extremely difficult task because of heavy noise by measurement error. Most of the recent clustering techniques for breast cancer database to achieve cancerous and noncancerous often weigh down the interpretability of the structure. Hence, this paper tries to find effective Fuzzy C-Means-based clustering techniques to identify the proper subtypes of cancer in breast cancer database. This paper obtains the objective function of effective Fuzzy C-Means clustering techniques by incorporating the kernel induced distance function, Renyi's entropy function, weighted distance measure and neighborhood terms-based spatial context. The effectiveness of the proposed methods are proved through the experimental works on Lung cancer database, IRIS dataset, Wine dataset, Checkerboard dataset, Time Series dataset and Yeast dataset. Finally, the proposed methods are implemented successfully to cluster the breast cancer database into cancerous and noncancerous. The clustering accuracy has been validated through error matrix and silhouette method.

scholarly journals Clustering Techniques for Medical Imaging

2019 ◽  
Vol 9 (2S) ◽  
pp. 510-516
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Medical Imaging ◽  
Swarm Optimization ◽  
Clustering Techniques ◽  
Brain Image ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Density Based Clustering ◽  
Fuzzy C Means Clustering

Nowadays medical imaging is becoming one of the popular techniques used to monitor human body to diagnose diseases, detect and treat injuries so that it can be treated. It helps in fetching desired information from the medical images. Clustering techniques in medical imaging is used to assist image based analysis of heterogeneous ailments by creating clusters of given population into homogeneous sub populations which helps in better understanding of the disease within each sub population. In this paper, we have discussed and compared various clustering techniques such as Fuzzy C Means clustering (FCM), Spatial Fuzzy C Means clustering(SFCM), K-Means and Particle Swarm Optimization Incorporative Fuzzy C Means clustering (PSOFCM), Gustafson Kessel (GK) clustering and Density Based Clustering of Applications with Noise (DBSCAN) to detect a tumor in human brain based on various image segmentation parameters. Accuracy of these algorithms is tested using MRI brain image.

Adaptive Clustering Techniques and Their Applications

2017 ◽  
pp. 380-397
Author(s):  
Deepthi P. Hudedagaddi ◽  
B. K. Tripathy
Keyword(s):  
Data Clustering ◽  
Clustering Algorithms ◽  
Efficient Technique ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Adaptive Clustering ◽  
Intuitionistic Fuzzy ◽  
Large Scope ◽  
Fuzzy C Means Clustering ◽  
Clustering Data

With the increasing volume of data, developing techniques to handle it has become the need of the hour. One such efficient technique is clustering. Data clustering is under vigorous development. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. Several data clustering algorithms have been developed in this regard. Data is uncertain and vague. Hence uncertain and hybrid based clustering algorithms like fuzzy c means, intuitionistic fuzzy c means, rough c means, rough intuitionistic fuzzy c means are being used. However, with the application and nature of data, clustering algorithms which adapt to the need are being used. These are nothing but the variations in existing techniques to match a particular scenario. The area of adaptive clustering algorithms is unexplored to a very large extent and hence has a large scope of research. Adaptive clustering algorithms are useful in areas where the situations keep on changing. Some of the adaptive fuzzy c means clustering algorithms are detailed in this chapter.

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