Comparison of Four Kinds of Fuzzy C-Means Clustering Methods

2010 ◽  
Author(s):  
Zengfeng Wang
Keyword(s):  
Clustering Methods ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

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.

Detection of Mobile Phone Fraud Using Possibilistic Fuzzy C-Means Clustering and Hidden Markov Model

2016 ◽  
Vol 7 (2) ◽  
pp. 23-44 ◽  
Author(s):  
Sharmila Subudhi ◽  
Suvasini Panigrahi ◽  
Tanmay Kumar Behera
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Mobile Phone ◽  
Hidden Markov ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Novel Approach ◽  
Incoming Call ◽  
Fuzzy C Means Clustering ◽  
Reality Mining

This paper presents a novel approach for fraud detection in mobile phone networks by using a combination of Possibilistic Fuzzy C-Means clustering and Hidden Markov Model (HMM). The clustering technique is first applied on two calling features extracted from the past call records of a subscriber generating a behavioral profile for the user. The HMM parameters are computed from the profile, which are used to generate some profile sequences for training. The trained HMM model is then applied for detecting fraudulent activities on incoming call sequences. A calling instance is detected as forged when the new sequence is not accepted by the trained model with sufficiently high probability. The efficacy of the proposed system is demonstrated by extensive experiments carried out with Reality Mining dataset. Furthermore, the comparative analysis performed with other clustering methods and another approach recently proposed in the literature justifies the effectiveness of the proposed algorithm.

Comparison of 3-point dixon imaging and fuzzy C-means clustering methods for breast density measurement

2013 ◽  
Vol 38 (2) ◽  
pp. 474-481 ◽  
Author(s):  
Tess V. Clendenen ◽  
Anne Zeleniuch-Jacquotte ◽  
Linda Moy ◽  
Malcolm C. Pike ◽  
Henry Rusinek ◽  
...  
Keyword(s):  
Breast Density ◽  
Density Measurement ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Dixon Imaging ◽  
Breast Density Measurement

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.

scholarly journals Optimization of Fuzzy c-means (FCM) Clustering in Cytology Image Segmentation Using the Gray Wolf Algorithm

2021 ◽  
Author(s):  
Maryam Mohammdian-khoshnoud ◽  
Ali Reza Soltanian ◽  
Arash Dehghan ◽  
Maryam Farhadian
Keyword(s):  
Image Segmentation ◽  
Gray Wolf ◽  
Clustering Methods ◽  
Local Optima ◽  
Fuzzy C Means ◽  
Significant Difference ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
The Common ◽  
Better Than

Abstract Background: Image segmentation is considered an important step in image processing. Fuzzy c-means clustering is one of the common methods of image segmentation. However, this method suffers from drawbacks, such as sensitivity to initial values, entrapment in local optima, and the inability to distinguish objects with similar color intensity. This paper proposes the hybrid Fuzzy c-means clustering and Gray wolf optimization for image segmentation to overcome the shortcomings of fuzzy c-means clustering. The Gray wolf optimizationhas a high exploration capability in finding the best solution to the problem, which prevents the entrapment of the algorithm in local optima. In this study, breast cytology images were used to validate the methods, and the results of the proposed method were compared to those of c-means clustering.Results: FCMGWO has performed better than FCM in separating the nucleus from the other dark objects in the cell. The clustering was validated using Vpc, Vpe, Davies-Bouldin, and Calinski Harabasz criteria. The FCM and FCMGWO methods have a significant difference with respect to the Vpc and Vpe indices. However, there is no significant difference between the performances of the two clustering methods with respect to the Calinski-Harabasz and Davies-Bouldinindices. The results indicate the better efficacy of the proposed method.Conclusions: The hybridFCMGWO algorithm distinguishes the cells better in images with less detail than in images with high detail. However, FCM exhibits unacceptable performance in both low- and high-detail images.

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