A new approach to session identification by applying fuzzy c-means clustering on web logs

2016 ◽  
Author(s):  
Dimitrios Koutsoukos ◽  
Georgios Alexandridis ◽  
Georgios Siolas ◽  
Andreas Stafylopatis
Keyword(s):  
New Approach ◽  
Fuzzy C Means ◽  
Web Logs ◽  
Fuzzy C Means Clustering

Kernel fuzzy C- means clustering with teaching learning based optimization algorithm (TLBO-KFCM)

2021 ◽  
pp. 1-9
Author(s):  
Saumya Singh ◽  
Smriti Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Data Analysis ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Data Sets ◽  
New Approach ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM).

scholarly journals Recognition of Power Quality Disturbances using Fuzzy Expert Systems

2021 ◽  
Vol 16 ◽  
pp. 166-177
Author(s):  
P. Kanirajan ◽  
M. Joly ◽  
T. Eswaran
Keyword(s):  
Power Quality ◽  
Classification Performance ◽  
New Approach ◽  
Fuzzy C Means ◽  
Fitness Value ◽  
The Social ◽  
Fuzzy Expert Systems ◽  
Fuzzy C Means Clustering ◽  
Power Quality Disturbances ◽  
Extensive Computation

This paper presents a new approach to detect and classify power quality disturbances in the power system using Fuzzy C-means clustering, Fuzzy logic (FL) and Radial basis Function Neural Networks (RBFNN). Feature extracted through wavelet is used for training, after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification,5 types of disturbance are taken in to account. The classification performance of FL is compared with RBFNN.The clustering analysis is used to group the data in to clusters to identifying the class of the data with Fuzzy C-means algorithm. The classification accuracy of FL and Fuzzy C-means clustering is improved with the help of cognitive as well as the social behavior of particles along with fitness value using Particle swarm optimization (PSO),just by determining the ranges of the feature of the membership funtion for each rules to identify each disturbance specifically.The simulation result using Fuzzy C-means clustering possess significant improvements and gives classification results in less than a cycle when compared over other considered approach.

3D magnetization inversion using fuzzy c-means clustering with application to geology differentiation

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. J61-J78 ◽  
Author(s):  
Yaoguo Li ◽  
Jiajia Sun
Keyword(s):  
Magnetic Anomaly ◽  
Remanent Magnetization ◽  
Data Sets ◽  
Total Field ◽  
Magnetic Interpretation ◽  
New Approach ◽  
Fuzzy C Means ◽  
Magnetic Inversion ◽  
Fuzzy C Means Clustering ◽  
Distribution Of Magnetization

The presence of remanent magnetization has hindered the application of generalized 3D magnetic inversion in exploration geophysics because of the unknown and variable magnetization directions. Although many authors have developed different approaches to deal with this difficulty, it remains a challenge. We have developed a new approach for inverting the total-field magnetic anomaly to recover a 3D distribution of magnetization by using a fuzzy c-means clustering technique. The inversion approximates the variation of magnetization directions with a small number of possible orientations and thereby achieves stability in recovered magnetization directions and improves the spatial imaging of magnetization magnitude. We have also found that the inverted magnetization directions can yield more information than does a standard magnetic susceptibility inversion and provide a new opportunity for magnetic interpretation. The magnitude of magnetization helps to define the configuration and structure of causative bodies in 3D, whereas the magnetization directions can help distinguish between different causative bodies and thereby assist in efforts such as geology differentiation. Thus, 3D magnetization inversion enables the complete use of the magnetic anomaly in the presence of remanent magnetization. We have used synthetic and field data sets to illustrate the algorithm, demonstrate the feasibility of geology differentiation using recovered magnetization directions, and develop a means to quantify the confidence of differentiation results.

scholarly journals Fuzzy Expert System for Recognition of Power Quality Disturbances

2020 ◽  
Vol 11 ◽  
pp. 60-71
Author(s):  
P. Kanirajan ◽  
M. Joly
Keyword(s):  
Power Quality ◽  
Clustering Analysis ◽  
Classification Performance ◽  
New Approach ◽  
Fuzzy C Means ◽  
Fitness Value ◽  
The Social ◽  
Fuzzy C Means Clustering ◽  
Power Quality Disturbances ◽  
Extensive Computation

This paper presents a new approach to detect and classify power quality disturbances in the power system using Fuzzy C-means clustering, Fuzzy logic (FL) and Radial basis Function Neural Networks (RBFNN). Feature extracted through wavelet is used for training, after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification,5 types of disturbance are taken in to account. The classification performance of FL is compared with RBFNN.The clustering analysis is used to group the data in to clusters to identifying the class of the data with Fuzzy C-means algorithm. The classification accuracy of FL and Fuzzy C-means clustering is improved with the help of cognitive as well as the social behavior of particles along with fitness value using Particle swarm optimization (PSO),just by determining the ranges of the feature of the membership funtion for each rules to identify each disturbance specifically.The simulation result using Fuzzy C-means clustering possess significant improvements and gives classification results in less than a cycle when compared over other considered approach.

A new weighted fuzzy C-means clustering approach considering between-cluster separability

2021 ◽  
Vol 40 (1) ◽  
pp. 1017-1024
Author(s):  
Ziheng Wu ◽  
Cong Li ◽  
Fang Zhou ◽  
Lei Liu
Keyword(s):  
Clustering Algorithm ◽  
Feature Weighting ◽  
Weighting Method ◽  
New Approach ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Clustering Approach ◽  
Cluster Separability ◽  
Cluster Compactness ◽  
Real World Datasets

Fuzzy C-means clustering algorithm (FCM) is an effective approach for clustering. However, in most existing FCM type frameworks, only in-cluster compactness is taken into account, whereas the between-cluster separability is overlooked. In this paper, to enhance the clustering, by incorporating the feature weighting and data weighting method, we put forward a new weighted fuzzy C-means clustering approach considering between-cluster separability, in which for achieving good compactness and separability, making the in-cluster distances as small as possible and making the between-cluster distances as large as possible, the in-cluster distances and between-cluster distances are taken into account; To achieve the optimal clustering result, the iterative formulas of the feature weights, membership degrees, data weights and cluster centers are obtained by maximizing the in-cluster compactness and the between-cluster separability. Experiments on real-world datasets were carried out, the results showed that the new approach could obtain promising performance.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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