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 A new approach to the fuzzy c-means clustering algorithm by automatic weights and local clustering

2021 ◽  
Vol 3 (1) ◽  
pp. 1-7
Author(s):  
Yadgar Sirwan Abdulrahman
Keyword(s):  
Clustering Algorithm ◽  
Similarity Criterion ◽  
Real Data ◽  
Well Being ◽  
Classical Solutions ◽  
Data Sets ◽  
Data Set ◽  
New Approach ◽  
Fuzzy C Means Clustering ◽  
Global And Local

Clustering is one of the essential strategies in data analysis. In classical solutions, all features are assumed to contribute equally to the data clustering. Of course, some features are more important than others in real data sets. As a result, essential features will have a more significant impact on identifying optimal clusters than other features. In this article, a fuzzy clustering algorithm with local automatic weighting is presented. The proposed algorithm has many advantages such as: 1) the weights perform features locally, meaning that each cluster's weight is different from the rest. 2) calculating the distance between the samples using a non-euclidian similarity criterion to reduce the noise effect. 3) the weight of the features is obtained comparatively during the learning process. In this study, mathematical analyzes were done to obtain the clustering centers well-being and the features' weights. Experiments were done on the data set range to represent the progressive algorithm's efficiency compared to other proposed algorithms with global and local features

scholarly journals AN EFFICIENTFUZZY C-MEANS CLUSTERING ALGORITHM FOR MULTI-VALUED DATA SETS

2021 ◽  
Vol 9 (1) ◽  
pp. 1250-1264
Author(s):  
P Gopala Krishna, D Lalitha Bhaskari
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Similar Data ◽  
Job Descriptions ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Ship Function ◽  
Partition Clustering ◽  
Diverse Data

In data analysis, items were mostly described by a set of characteristics called features, in which each feature contains only single value for each object. Even so, in existence, some features may include more than one value, such as a person with different job descriptions, activities, phone numbers, skills and different mailing addresses. Such features may be called as multi-valued features, and are mostly classified as null features while analyzing the data using machine learning and data mining techniques.  In this paper, it is proposed a proximity function to be described between two substances with multi-valued features that are put into effect for clustering.The suggested distance approach allows iterative measurements of the similarities around objects as well as their characteristics. For facilitating the most suitable multi-valued factors, we put forward a model targeting at determining each factor’s relative prominence for diverse data extracting problems. The proposed algorithm is a partition clustering strategy that uses fuzzy c- means clustering for evolutions, which is using the novel member ship function by utilizing the proposed similarity measure. The proposed clustering algorithm as fuzzy c- means based Clustering of Multivalued Attribute Data (FCM-MVA).Therefore this becomes feasible using any mechanisms for cluster analysis to group similar data. The findings demonstrate that our test not only improves the performance the traditional measure of similarity but also outperforms other clustering algorithms on the multi-valued clustering framework.  

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 Neural Network Based Fuzzy C-MEANS Clustering Algorithm

2011 ◽  
pp. 100-104
Author(s):  
Suneetha Chittinen ◽  
Dr. Raveendra Babu Bhogapathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Fast Learning ◽  
Presentation Sequence ◽  
Fuzzy C Means Clustering ◽  
Artificial Neural

In this paper, fuzzy c-means algorithm uses neural network algorithm is presented. In pattern recognition, fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms to group the high dimensional data into clusters. The proposed work involves two steps. First, a recently developed and Enhanced Kmeans Fast Leaning Artificial Neural Network (KFLANN) frame work is used to determine cluster centers. Secondly, Fuzzy C-means uses these cluster centers to generate fuzzy membership functions. Enhanced K-means Fast Learning Artificial Neural Network (KFLANN) is an algorithm which produces consistent classification of the vectors in to the same clusters regardless of the data presentation sequence. Experiments are conducted on two artificial data sets Iris and New Thyroid. The result shows that Enhanced KFLANN is faster to generate consistent cluster centers and utilizes these for elicitation of efficient fuzzy memberships.

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.

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