Collaborative Fuzzy Clustering Algorithms: Some Refinements and Design Guidelines

2012 ◽  
Vol 20 (3) ◽  
pp. 444-462 ◽  
Author(s):  
Luiz F. S. Coletta ◽  
Lucas Vendramin ◽  
Eduardo Raul Hruschka ◽  
Ricardo J. G. B. Campello ◽  
Witold Pedrycz
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithms ◽  
Design Guidelines

AN EXTENDED FUZZY CLUSTERING ALGORITHM AND ITS APPLICATION

1995 ◽  
Vol 05 (02) ◽  
pp. 239-259
Author(s):  
SU HWAN KIM ◽  
SEON WOOK KIM ◽  
TAE WON RHEE
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Main Memory ◽  
Color Image Segmentation ◽  
Occurrence Rate ◽  
Secondary Memory ◽  
Worst Case ◽  
Memory Space ◽  
Fuzzy Clustering Algorithm

For data analyses, it is very important to combine data with similar attribute values into a categorically homogeneous subset, called a cluster, and this technique is called clustering. Generally crisp clustering algorithms are weak in noise, because each datum should be assigned to exactly one cluster. In order to solve the problem, a fuzzy c-means, a fuzzy maximum likelihood estimation, and an optimal fuzzy clustering algorithms in the fuzzy set theory have been proposed. They, however, require a lot of processing time because of exhaustive iteration with an amount of data and their memberships. Especially large memory space results in the degradation of performance in real-time processing applications, because it takes too much time to swap between the main memory and the secondary memory. To overcome these limitations, an extended fuzzy clustering algorithm based on an unsupervised optimal fuzzy clustering algorithm is proposed in this paper. This algorithm assigns a weight factor to each distinct datum considering its occurrence rate. Also, the proposed extended fuzzy clustering algorithm considers the degree of importances of each attribute, which determines the characteristics of the data. The worst case is that the whole data has an uniformly normal distribution, which means the importance of all attributes are the same. The proposed extended fuzzy clustering algorithm has better performance than the unsupervised optimal fuzzy clustering algorithm in terms of memory space and execution time in most cases. For simulation the proposed algorithm is applied to color image segmentation. Also automatic target detection and multipeak detection are considered as applications. These schemes can be applied to any other fuzzy clustering algorithms.

Construct Knowledge Structure of Linear Algebra

2011 ◽  
Vol 211-212 ◽  
pp. 793-797
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih ◽  
Sue Fen Huang
Keyword(s):  
Knowledge Management ◽  
Linear Algebra ◽  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Clustering Algorithms ◽  
Knowledge Structure ◽  
Interpretive Structural Modeling ◽  
Cognitive Characteristics ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzy clustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuzzy c-means algorithm. Each cluster of data can easily describe features of knowledge structures individually. The results show that there are six clusters and each cluster has its own cognitive characteristics. The methodology can improve knowledge management in classroom more feasible.

Kernel-Based Fuzzy Local Information Clustering Algorithm Self-integrating Non-Local Information

2021 ◽  
Author(s):  
Qiuyu Song ◽  
Chengmao Wu ◽  
Xiaoping Tian ◽  
Yue Song ◽  
Xiaokang Guo
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Integration Method ◽  
Clustering Algorithms ◽  
Noise Removal ◽  
Local Information ◽  
Gaussian Kernel ◽  
Distance Metric ◽  
High Noise ◽  
Non Local

Abstract The application of fuzzy clustering algorithms in image segmentation is a hot research topic nowadays. Existing fuzzy clustering algorithms have the following three problems: (1)The parameters of spatial information constraints can$'$t be selected adaptively; (2)The image corrupted by high noise can$'$t be segmented effectively; (3)It is difficult to achieve a balance between noise removal and detail preservation. In the fuzzy clustering based on the optimization model, the choice of distance metric is very important. Since the use of Euclidean distance will lead to sensitivity to outliers and noise, it is difficult to obtain satisfactory segmentation results, which will affect the clustering performance. This paper proposes an optimization algorithm based on the kernel-based fuzzy local information clustering integrating non-local information (KFLNLI). The algorithm adopts a self-integration method to introduce local and non-local information of images, which solves the common problems of current clustering algorithm. Firstly, the self-integration method solves the problem of selecting spatial constraint parameters. The algorithm uses continuous self-learning iteration to calculate the weight coefficients; Secondly, the distance metric uses Gaussian kernel function to induce the distance to further enhance the robustness against noise and the adaptivity of processing different images; Finally, both local and non-local information are introduced to achieve a segmentation effect that can eliminate most of the noise and retain the original details of the image. Experimental results show that the algorithm is superior to existing state-of-the-art fuzzy clustering-related algorithm in the presence of high noise.

Data Clustering Algorithms Using Rough Sets

2013 ◽  
pp. 297-327 ◽  
Author(s):  
B.K. Tripathy ◽  
Adhir Ghosh
Keyword(s):  
Comparative Study ◽  
Rough Set ◽  
Fuzzy Clustering ◽  
Fuzzy Set ◽  
Rough Sets ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Future Studies ◽  
Multiple Clusters

Developing Data Clustering algorithms have been pursued by researchers since the introduction of k-means algorithm (Macqueen 1967; Lloyd 1982). These algorithms were subsequently modified to handle categorical data. In order to handle the situations where objects can have memberships in multiple clusters, fuzzy clustering and rough clustering methods were introduced (Lingras et al 2003, 2004a). There are many extensions of these initial algorithms (Lingras et al 2004b; Lingras 2007; Mitra 2004; Peters 2006, 2007). The MMR algorithm (Parmar et al 2007), its extensions (Tripathy et al 2009, 2011a, 2011b) and the MADE algorithm (Herawan et al 2010) use rough set techniques for clustering. In this chapter, the authors focus on rough set based clustering algorithms and provide a comparative study of all the fuzzy set based and rough set based clustering algorithms in terms of their efficiency. They also present problems for future studies in the direction of the topics covered.

Fuzzy Logic Applied to Biomedical Image Analysis

2011 ◽  
pp. 710-718 ◽  
Author(s):  
Alfonso Castro ◽  
Bernardino Arcay
Keyword(s):  
Fuzzy Logic ◽  
Image Analysis ◽  
Fuzzy Clustering ◽  
Intelligent Systems ◽  
Clustering Algorithms ◽  
Kernel Functions ◽  
Point Of View ◽  
Industrial Robots ◽  
Biomedical Image ◽  
Research Areas

Ever since Zadeh established the basis of fuzzy logic in his famous article Fuzzy Sets (Zadeh, 1965), an increasing number of research areas have used his technique to solve and model problems and apply it, mainly, to control systems. This proliferation is largely due to the good results in classifying the ambiguous information that is typical of complex systems. Success in this field has been so overwhelming that it can be found in many industrial developments of the last decade: control of the Sendai train (Yasunobu & Miyamoto, 1985), control of air-conditioning systems, washing machines, auto-focus in cameras, industrial robots, etc. (Shaw, 1998) Fuzzy logic has also been applied to computerized image analysis (Bezdek & Keller & Krishnapuram & Pal, 1999) because of its particular virtues: high noise insensitivity and the ability to easily handle multidimensional information (Sutton & Bezdek & Cahoon, 1999), features that are present in most digital images analyses. In fuzzy logic, the techniques that have been most often applied to image analysis have been fuzzy clustering algorithms, ever since Bezdek proposed them in the seventies (Bezdek, 1973). This technique has evolved continuously towards correcting the problems of the initial algorithms and obtaining a better classification: techniques for a better initialization of these algorithms, and algorithms that would allow the evaluation of the solution by means of validity functions. Also, the classification mechanism was improved by modifying the membership function of the algorithm, allowing it to present an adaptative behaviour; recently, kernel functions were applied to the calculation of memberships. (Zhong & Wei & Jian, 2003) At the present moment, applications of fuzzy logic are found in nearly all Computer Sciences fields, it constitutes one of the most promising branches of Artificial Intelligence both from a theoretic and commercial point of view. A proof of this evolution is the development of intelligent systems based on fuzzy logic. This article presents several fuzzy clustering algorithms applied to medical images analysis. We also include the results of a study that uses biomedical images to illustrate the mentioned concepts and techniques.

scholarly journals An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Dong ◽  
Hao Jia ◽  
Miao Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Majority Vote ◽  
Clustering Algorithms ◽  
Nsga Ii ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy Clustering Method

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

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