Extended fuzzy c-means: an analyzing data clustering problems

2012 ◽  
Vol 16 (3) ◽  
pp. 389-406 ◽  
Author(s):  
S. Ramathilagam ◽  
R. Devi ◽  
S. R. Kannan
Keyword(s):  
Data Clustering ◽  
Fuzzy C Means ◽  
Clustering Problems

Effective fuzzy c-means clustering algorithms for data clustering problems

2012 ◽  
Vol 39 (7) ◽  
pp. 6292-6300 ◽  
Author(s):  
S.R. Kannan ◽  
S. Ramathilagam ◽  
P.C. Chung
Keyword(s):  
Data Clustering ◽  
Clustering Algorithms ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Clustering Problems

Novel Quadratic Fuzzy c-Means Algorithms for Effective Data Clustering Problems

2012 ◽  
Vol 56 (3) ◽  
pp. 393-406
Author(s):  
S. R. Kannan ◽  
S. Ramthilagam ◽  
R. Devi ◽  
Y.-M. Huang
Keyword(s):  
Data Clustering ◽  
Fuzzy C Means ◽  
Clustering Problems

scholarly journals Penentuan Cluster Optimum pada Tingkat Pengangguran dan Tingkat Kemiskinan di Jawa Timur Menggunakan Algoritma Fuzzy C-Means

2021 ◽  
Vol 1 (2) ◽  
pp. 40-47
Author(s):  
Ari Eko Wardoyo ◽  
Nigati Tripuspita
Keyword(s):  
Unemployment Rate ◽  
Data Clustering ◽  
Research Process ◽  
Poverty Rate ◽  
Fuzzy C Means ◽  
Central Statistical ◽  
The Government ◽  
Clustering Problems ◽  
Statistical Agency

There are many methods used in resolving data clustering. One of them is the Fuzzy C-Means (FCM) method, which is a reliable method to solve clustering problems in the East Java region. This study aims to determine the optimum cluster in the East Java region which can help the government to identify problems and assist policymaking in regencies/cities in East Java province. The research process uses data from the central statistical agency, namely the unemployment rate and poverty rate from 2010 to 2015. In this study, the Davies Bouldin Index (DBI) is used as a cluster validation test for determining the optimum cluster. Unemployment rate and poverty rate data were analyzed using RStudio. From the calculation of the FCM method and also the determination of the optimum cluster results obtained in 2 clusters with a DBI value of 1.2759, 3 clusters with a DBI value of 0.9937, 4 clusters with a DBI value of 0.8737. The optimum cluster is in 4 clusters with a minimum DBI value.

An Evolutionary Multi-Layer Extreme Learning Machine for Data Clustering Problems

2021 ◽  
Author(s):  
Xian Wu ◽  
Tianfang Zhou ◽  
Kaixiang Yi ◽  
Minrui Fei ◽  
Yayu Chen ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Data Clustering ◽  
Learning Machine ◽  
Clustering Problems

scholarly journals Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

Uncertainty-Based Clustering Algorithms for Large Data Sets

2018 ◽  
pp. 1-33 ◽  
Author(s):  
B. K. Tripathy ◽  
Hari Seetha ◽  
M. N. Murty
Keyword(s):  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Large Data Sets ◽  
Mining Machine ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
New Algorithms

Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in this direction. These algorithms are fuzzy c-means, rough c-means, intuitionistic fuzzy c-means and the means like rough fuzzy c-means, rough intuitionistic fuzzy c-means which base on hybrid models. Also, we find many variants of these algorithms which improve them in different directions like their Kernelised versions, possibilistic versions, and possibilistic Kernelised versions. However, all the above algorithms are not effective on big data for various reasons. So, researchers have been trying for the past few years to improve these algorithms in order they can be applied to cluster big data. The algorithms are relatively few in comparison to those for datasets of reasonable size. It is our aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further.

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