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.

A Novel Fuzzy Clustering Method with No Outliers Influence

2013 ◽  
Vol 300-301 ◽  
pp. 735-739 ◽  
Author(s):  
Li Jen Kao ◽  
Yo Ping Huang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Human Robot Interaction ◽  
Hand Gesture ◽  
Clustering Method ◽  
Robot Interaction ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Fcm Clustering

Fuzzy C-Means (FCM) clustering algorithm can be used to classify hand gesture images in human-robot interaction application. However, FCM algorithm does not work well on those images in which noises exist. The noises or outliers make all the cluster centers towards to the center of all points. In this paper, a new FCM algorithm is proposed to detect the outliers and then make the outliers have no influence on centers calculation. The experiment shows that the new FCM algorithm can get more accurate centers than the traditional FCM algorithm.

Improving Image Search through MKFCM Clustering Strategy-Based Re-ranking Measure

2018 ◽  
Vol 29 (1) ◽  
pp. 497-514
Author(s):  
A.K. Naveena ◽  
N.K. Narayanan
Keyword(s):  
Clustering Algorithm ◽  
Data Retrieval ◽  
Initial Step ◽  
Discrete Wavelet ◽  
Image Search ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Image Retrieval System ◽  
Fuzzy C Means Clustering ◽  
Search Approach

Abstract The main intention of this research is to develop a novel ranking measure for content-based image retrieval system. Owing to the achievement of data retrieval, most commercial search engines still utilize a text-based search approach for image search by utilizing encompassing textual information. As the text information is, in some cases, noisy and even inaccessible, the drawback of such a recovery strategy is to the extent that it cannot depict the contents of images precisely, subsequently hampering the execution of image search. In order to improve the performance of image search, we propose in this work a novel algorithm for improving image search through a multi-kernel fuzzy c-means (MKFCM) algorithm. In the initial step of our method, images are retrieved using four-level discrete wavelet transform-based features and the MKFCM clustering algorithm. Next, the retrieved images are analyzed using fuzzy c-means clustering methods, and the rank of the results is adjusted according to the distance of a cluster from a query. To improve the ranking performance, we combine the retrieved result and ranking result. At last, we obtain the ranked retrieved images. In addition, we analyze the effects of different clustering methods. The effectiveness of the proposed methodology is analyzed with the help of precision, recall, and F-measures.

Extension-Based Clustering Method: An Approach to Support Adaptable Design of the Product

2007 ◽  
Author(s):  
Yanwei Zhao ◽  
Huijun Tang ◽  
Nan Su ◽  
Wanliang Wang
Keyword(s):  
Fuzzy Clustering ◽  
Design Method ◽  
Direct Method ◽  
Threshold Value ◽  
Minimal Distance ◽  
Clustering Methods ◽  
Clustering Method ◽  
Adaptable Design ◽  
Fuzzy Clustering Method ◽  
Distance Formula

Design for product adaptability is one of the techniques used to provide customers with products that exactly meet their requirements. Clustering methods have been used extensively in the study of product adaptability design. Of the clustering methods, the fuzzy clustering method is the most widely in the design field. The three main kinds of fuzzy clustering methods are the transitive closure method, the dynamic direct method and the maximum tree method. The dynamic direct clustering method has been found to produce design solutions with the lowest cost. In this paper, a new approach for obtaining adaptable product designs using the clustering method is proposed. The method consists of three steps. Firstly, the extension distance formula is used to determine the distance between two products in a product database. The product design space and the distances between individuals are used as grouping criteria in this step. Secondly, the minimal distance between products is used to obtain the clustering index. Thirdly, the threshold value is used to divide the products in the database into groups. Customer demands and the results obtained from the adaptable function (based on the extension distance formula) are used to evaluate the fitness of the groups and their corresponding products. The product with the largest adaptable function value to demand ratio is selected product. In order to the show the advantage of using the extension-clustering method, both the extension-clustering method and the dynamic direct method are presented and compared. The comparison indicates that the extension-clustering method leads to quicker evaluations of design alternatives and results that more closely match customers’ demands. An example of the adaptable design of circular saws tools is used to demonstrate that with the extension-clustering design method a high variety of intelligent configurations can be obtained with significant rapidity.

Fuzzified Even-Sized Clustering Based on Optimization

2018 ◽  
Vol 22 (4) ◽  
pp. 537-543 ◽  
Author(s):  
Kei Kitajima ◽  
Yasunori Endo ◽  
Yukihiro Hamasuna ◽  
◽  
◽  
...  
Keyword(s):  
Data Analysis ◽  
Fuzzy Clustering ◽  
Cluster Size ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Numerical Examples ◽  
Fuzzy Clustering Method

Clustering is a method of data analysis without the use of supervised data. Even-sized clustering based on optimization (ECBO) is a clustering algorithm that focuses on cluster size with the constraints that cluster sizes must be the same. However, this constraints makes ECBO inconvenient to apply in cases where a certain margin of cluster size is allowed. It is believed that this issue can be overcome by applying a fuzzy clustering method. Fuzzy clustering can represent the membership of data to clusters more flexible. In this paper, we propose a new even-sized clustering algorithm based on fuzzy clustering and verify its effectiveness through numerical examples.

scholarly journals A Novel Semi-Supervised Fuzzy C-Means Clustering Algorithm Using Multiple Fuzzification Coefficients

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 258
Author(s):  
Tran Dinh Khang ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Machine Learning Techniques ◽  
Unsupervised Machine Learning ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Learning Techniques ◽  
Fuzzy C Means Clustering ◽  
Data Points ◽  
Data Elements

Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering belongs to the group of unsupervised machine learning techniques, meaning that there is no information about the labels of the elements. However, when knowledge of data points is known in advance, it will be beneficial to use a semi-supervised algorithm. Within many clustering techniques available, fuzzy C-means clustering (FCM) is a common one. To make the FCM algorithm a semi-supervised method, it was proposed in the literature to use an auxiliary matrix to adjust the membership grade of the elements to force them into certain clusters during the computation. In this study, instead of using the auxiliary matrix, we proposed to use multiple fuzzification coefficients to implement the semi-supervision component. After deriving the proposed semi-supervised fuzzy C-means clustering algorithm with multiple fuzzification coefficients (sSMC-FCM), we demonstrated the convergence of the algorithm and validated the efficiency of the method through a numerical example.

Research on the Optimization Model of Fault Detection for Large-Scale Electronic Circuit

2014 ◽  
Vol 986-987 ◽  
pp. 1579-1582
Author(s):  
Bao Zhen Feng
Keyword(s):  
Fault Detection ◽  
Fuzzy Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Electronic Circuit ◽  
Information Filtering ◽  
Clustering Method ◽  
Error Message ◽  
Fuzzy Clustering Method ◽  
Fault Information

In current large-scale electronic circuit devices, failure data calibration capacity is not strong and it is difficult to be precise classification and intelligent judgment. It lacks of the necessary mechanisms to eliminate the error message, bringing troubles to fault detection. In order to avoid the above defect, this paper presents a fault detection method for large-scale electronic circuit based on fuzzy clustering algorithm. Firstly, the use of means clustering method, the fault information is made initial classification. Then, using the second fuzzy clustering method make fault information filtering in different categories, in order to achieve the fault data confirmation. Experimental results show that the proposed algorithm can effectively improve the accuracy of fault detection of large-scale electronic circuit.

FUZZY CLUSTERING WITH WEIGHTING OF DATA VARIABLES

2000 ◽  
Vol 08 (06) ◽  
pp. 735-746 ◽  
Author(s):  
ANNETTE KELLER ◽  
FRANK KLAWONN
Keyword(s):  
Objective Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Technique ◽  
Fuzzy C Means ◽  
Or Groups ◽  
Fuzzy C Means Clustering ◽  
Influence Parameter ◽  
Individual Variables ◽  
Single Data

We introduce an objective function-based fuzzy clustering technique that assigns one influence parameter to each single data variable for each cluster. Our method is not only suited to detect structures or groups of data that are not uniformly distributed over the structure's single domains, but gives also information about the influence of individual variables on the detected groups. In addition, our approach can be seen as a generalization of the well-known fuzzy c-means clustering algorithm.

scholarly journals ONLINE PROBABILISTIC FUZZY CLUSTERING METHOD BASED ON EVOLUTIONARY OPTIMIZATION OF CAT SWARM

2021 ◽  
pp. 65-70
Author(s):  
Ye. V. Bodyanskiy ◽  
A. Yu. Shafronenko ◽  
I. N. Klymova
Keyword(s):  
Big Data ◽  
Fuzzy Clustering ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Evolutionary Optimization ◽  
Clustering Methods ◽  
Classification Problems ◽  
Probabilistic Data ◽  
Fuzzy Clustering Method ◽  
Clustering And Classification

Context. The problems of big data clustering today is a very relevant area of artificial intelligence. This task is often found in many applications related to data mining, deep learning, etc. To solve these problems, traditional approaches and methods require that the entire data sample be submitted in batch form. Objective. The aim of the work is to propose a method of fuzzy probabilistic data clustering using evolutionary optimization of cat swarm, that would be devoid of the drawbacks of traditional data clustering approaches. Method. The procedure of fuzzy probabilistic data clustering using evolutionary algorithms, for faster determination of sample extrema, cluster centroids and adaptive functions, allowing not to spend machine resources for storing intermediate calculations and do not require additional time to solve the problem of data clustering, regardless of the dimension and the method of presentation for processing. Results. The proposed data clustering algorithm based on evolutionary optimization is simple in numerical implementation, is devoid of the drawbacks inherent in traditional fuzzy clustering methods and can work with a large size of input information processed online in real time. Conclusions. The results of the experiment allow to recommend the developed method for solving the problems of automatic clustering and classification of big data, as quickly as possible to find the extrema of the sample, regardless of the method of submitting the data for processing. The proposed method of online probabilistic fuzzy data clustering based on evolutionary optimization of cat swarm is intended for use in hybrid computational intelligence systems, neuro-fuzzy systems, in training artificial neural networks, in clustering and classification problems.

Fuzzy Clustering Method for Spherical Data Based on q-Divergence

2019 ◽  
Vol 23 (3) ◽  
pp. 561-570 ◽  
Author(s):  
Masayuki Higashi ◽  
◽  
Tadafumi Kondo ◽  
Yuchi Kanzawa
Keyword(s):  
Conventional Method ◽  
Fuzzy Clustering ◽  
Optimization Problem ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Kl Divergence ◽  
Spherical Data ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy Clustering Method ◽  
Vectorial Data

This study presents a fuzzy clustering algorithm for classifying spherical data based on q-divergence. First, it is shown that a conventional method for vectorial data is equivalent to the regularization of another conventional method using q-divergence. Next, based on the knowledge that q-divergence is a generalization of Kullback-Leibler (KL)-divergence and that there is a conventional fuzzy clustering method for classifying spherical data based on KL-divergence, a fuzzy clustering algorithm for spherical data is derived based on q-divergence. This algorithm uses an optimization problem built by extending KL-divergence in the conventional method to q-divergence. Finally, some numerical experiments are conducted to verify the proposed methods.

TEXTUAL-BASED CLUSTERING OF WEB DOCUMENTS

2004 ◽  
Vol 12 (06) ◽  
pp. 715-743 ◽  
Author(s):  
PAWEL BRZEMINSKI ◽  
WITOLD PEDRYCZ
Keyword(s):  
Experimental Study ◽  
Data Collection ◽  
Internal Structure ◽  
Clustering Algorithm ◽  
Web Pages ◽  
Clustering Method ◽  
Web Documents ◽  
Fuzzy C Means ◽  
Input Parameters ◽  
Fuzzy C Means Clustering

In our study we presented an effective method for clustering of Web pages. From flat HTML files we extracted keywords, formed feature vectors as representation of Web pages and applied them to a clustering method. We took advantage of the Fuzzy C-Means clustering algorithm (FCM). We demonstrated an organized and schematic manner of data collection. Various categories of Web pages were retrieved from ODP (Open Directory Project) in order to create our datasets. The results of clustering proved that the method performs well for all datasets. Finally, we presented a comprehensive experimental study examining: the behavior of the algorithm for different input parameters, internal structure of datasets and classification experiments.

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