An Interval Slope Approach to Fuzzy C-Means Clustering Algorithm for Interval Valued Data

2014 ◽  
Vol 989-994 ◽  
pp. 1641-1645
Author(s):  
Yan Jin ◽  
Jiang Hong Ma
Keyword(s):  
Clustering Algorithm ◽  
Interval Data ◽  
Experimental Results ◽  
Clustering Methods ◽  
Structure Information ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Interval Valued

Interval data is a range of continuous values which can describe the uncertainty. The traditional clustering methods ignore the structure information of intervals. And some modified ones have been developed. We have already used Taylor technique to perform well in the fuzzy c-means clustering algorithm. In this paper, we propose a new way based on the mixed interval slopes technique and interval computing. Experimental results also show the effectiveness of our approach.

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.

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.

Interval-valued possibilistic fuzzy C-means clustering algorithm

2014 ◽  
Vol 253 ◽  
pp. 138-156 ◽  
Author(s):  
Zexuan Ji ◽  
Yong Xia ◽  
Quansen Sun ◽  
Guo Cao
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Interval Valued

AN UNSUPERVISED KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM WITH KERNEL NORMALISATION

2004 ◽  
Vol 04 (04) ◽  
pp. 355-373 ◽  
Author(s):  
SHANG-MING ZHOU ◽  
JOHN Q. GAN
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Experimental Results ◽  
New Method ◽  
Fcm Algorithm ◽  
Input Space ◽  
Fuzzy C Means ◽  
Benchmark Datasets ◽  
Fuzzy C Means Clustering ◽  
Mercer Kernel

In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios.

scholarly journals A Weight Possibilistic Fuzzy C-Means Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiashun Chen ◽  
Hao Zhang ◽  
Dechang Pi ◽  
Mehmed Kantardzic ◽  
Qi Yin ◽  
...  
Keyword(s):  
Data Analysis ◽  
Stock Market ◽  
Objective Function ◽  
Clustering Algorithm ◽  
Lagrange Equation ◽  
Experimental Results ◽  
Retail Market ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. Especially, parameters in FCM have influence on clustering results. However, a lot of FCM algorithm did not solve the problem, that is, how to set parameters. In this study, we present a kind of method for computing parameters values according to role of parameters in the clustering process. New parameters are assigned to membership and typicality so as to modify objective function, on the basis of which Lagrange equation is constructed and iterative equation of membership is acquired, so does the typicality and center equation. At last, a new possibilistic fuzzy C-means based on the weight parameter algorithm (WPFCM) was proposed. In order to test the efficiency of the algorithm, some experiments on different datasets are conducted to compare WPFCM with FCM, possibilistic C-means (PCM), and possibilistic fuzzy C-means (PFCM). Experimental results show that iterative times of WPFCM are less than FCM about 25% and PFCM about 65% on dataset X12. Resubstitution errors of WPFCM are less than FCM about 19% and PCM about 74% and PFCM about 10% on the IRIS dataset.

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

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