scholarly journals Clustering Batik Images using Fuzzy C-Means Algorithm Based on Log-Average Luminance

2012 ◽  
Vol 1 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Ahmad Sanmorino
Keyword(s):  
Clustering Algorithm ◽  
Staining Technique ◽  
Image Clustering ◽  
Fuzzy C Means ◽  
Average Value ◽  
Artistic Value ◽  
Fuzzy Models ◽  
Average Luminance ◽  
Fcm Clustering ◽  
Fuzzy C Means Algorithm

Batik is a fabric or clothes that are made ​​with a special staining technique called wax-resist dyeing and is one of the cultural heritage which has high artistic value. In order to improve the efficiency and give better semantic to the image, some researchers apply clustering algorithm for managing images before they can be retrieved. Image clustering is a process of grouping images based on their similarity. In this paper we attempt to provide an alternative method of grouping batik image using fuzzy c-means (FCM) algorithm based on log-average luminance of the batik. FCM clustering algorithm is an algorithm that works using fuzzy models that allow all data from all cluster members are formed with different degrees of membership between 0 and 1. Log-average luminance (LAL) is the average value of the lighting in an image. We can compare different image lighting from one image to another using LAL. From the experiments that have been made, it can be concluded that fuzzy c-means algorithm can be used for batik image clustering based on log-average luminance of each image possessed.DOI: 10.18495/comengapp.11.025031

Improvement on a Fuzzy C-Means Algorithm Based on Genetic Algorithm

2014 ◽  
Vol 614 ◽  
pp. 385-388
Author(s):  
Guo Chen Jiang ◽  
Zhi Jian Sun
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Optimal Choice ◽  
Experimental Results ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
Fuzzy C Means Algorithm

Weighting exponent m is an important parameter in fuzzy c-means(FCM) algorithm. In this paper, an approach based on genetic algorithm is proposed to improve the FCM clustering algorithm through the optimal choice of the parameter m. Experimental results show that the better clustering results are obtained through the new algorithm.

Management of Abstract Algebra Concepts Based on Knowledge Structure

2013 ◽  
Vol 284-287 ◽  
pp. 3537-3542
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih
Keyword(s):  
Mahalanobis Distance ◽  
Clustering Algorithm ◽  
Fuzzy Theory ◽  
Clustering Algorithms ◽  
Knowledge Structures ◽  
Interpretive Structural Modeling ◽  
Fuzzy C Means ◽  
Integrated Method ◽  
Combined Algorithm ◽  
Fuzzy C Means Algorithm

Knowledge Management of Mathematics Concepts was essential in educational environment. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. A new improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) is proposed. Use the best performance of clustering Algorithm FCM-NM in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Mathematics Concepts to construct the model of features in the pattern recognition completely. This procedure will also useful for cognition diagnosis. To sum up, this integrated algorithm could improve the assessment methodology of cognition diagnosis and manage the knowledge structures of Mathematics Concepts easily.

scholarly journals Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

Weighting exponent m in fuzzy C-means (FCM) clustering algorithm

2001 ◽  
Author(s):  
Jihong Pei ◽  
Xuan Yang ◽  
Xinbo Gao ◽  
Weixing Xie
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy C Means ◽  
Fcm Clustering

scholarly journals A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 554 ◽  
Author(s):  
Barbara Cardone ◽  
Ferdinando Di Martino
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Fuzzy Entropy ◽  
Entropy Function ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Random Initialization ◽  
Fuzzy C Means Algorithm ◽  
Optimal Cluster

One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. In this paper we propose a variation of FCM in which the initial optimal cluster centers are obtained by implementing a weighted FCM algorithm in which the weights are assigned by calculating a Shannon Fuzzy Entropy function. The results of the comparison tests applied on various classification datasets of the UCI Machine Learning Repository show that our algorithm improved in all cases relating to the performances of FCM.

Application of Hard C-Means and Fuzzy C-Means in Data Fusion

2012 ◽  
Vol 190-191 ◽  
pp. 265-268
Author(s):  
Ai Hong Tang ◽  
Lian Cai ◽  
You Mei Zhang
Keyword(s):  
Data Fusion ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Large Data ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Data Similarity ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy C Means Algorithm

This article describes two kinds of Fuzzy clustering algorithm based on partition,Fuzzy C-means algorithm is on the basis of the hard C-means algorithm, and get a big improvement, making large data similarity as far as possible together. As a result of Simulation, FCM algorithm has more reasonable than HCM method on convergence, data fusion, and so on.

scholarly journals An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Elid Rubio ◽  
Oscar Castillo ◽  
Fevrier Valdez ◽  
Patricia Melin ◽  
Claudia I. Gonzalez ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Image Preprocessing ◽  
Cover Type ◽  
Fuzzy C Means ◽  
Possibilistic Clustering ◽  
Interval Type ◽  
Fuzzy C Means Algorithm

In this work an extension of the Fuzzy Possibilistic C-Means (FPCM) algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM) algorithm to observe if the proposed approach has better performance than this algorithm.

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