• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• Journal of the Korean Society of Safety
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• v.11 no.1
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• pp.39-45
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• 1996

In this study, clustering of black BADUK stones' image were performed to recognize the individual stone from its closely spaced and partially occluded Image. And the clustering perfomance was compared between the classical methods and fuzzy C-means method. As a result, 2 BADUK stones' image was segmented precisely in every methods, but more than 3 stones the segmentation was depended on its shape. Fuzzy C-means method could be segmented correctly to 4 stones regardless of its shape, and It could be applied to the unknown number of clusters.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.986-1016
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• 2019

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.529-535
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• 2014

In this paper, we propose a hybrid approach towards multiple kernels interval type-2 possibilistic fuzzy C-means(PFCM) based on interval type-2 possibilistic fuzzy c-means(IT2PFCM) and possibilistic fuzzy c-means using multiple kernels( PFCM-MK). In case of noisy data or overlapping cluster prototypes, fuzzy C-means gives poor performance in comparison to possibilistic fuzzy C-means(PFCM). Moreover, to address the uncertainty associated with fuzzifier parameter m, interval type-2 possibilistic fuzzy C-means(PFCM) is used. Most of the practical data available are complex and non-linearly separable. In such cases using Gaussian kernels proves helpful. Therefore, in order to overcome all these issues, we have integrated multiple kernels possibilistic fuzzy C-means(PFCM) into interval type-2 possibilistic fuzzy C-means(IT2PFCM) and propose the idea of multiple kernels based interval type-2 possibilistic fuzzy C-means(IT2PFCM-MK).

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.39-46
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• 2011

Clustering is one of the well-known unsupervised learning methods, in which a data set is grouped into some number of homogeneous clusters. There are numerous clustering algorithms available and they have been used in various applications. Fuzzy c-means (FCM), the most well-known partitional clustering algorithm, was established in 1970's and still in use. However, there are some unsolved problems in FCM and variants of FCM are still under development. In this paper, the problems in FCM are first explained and the available solutions are investigated, which is aimed to give researchers some possible ways of future research. Most of the FCM variants try to solve the problems using domain knowledge specific to a given problem. However, in this paper, we try to give general solutions without using any domain knowledge. Although there are more things left than discovered, this paper may be a good starting point for researchers newly entered into a clustering area.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.113-116
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• 2001

The conventional K-means algorithm is widely used in vector quantizer design and clustering analysis. Recently modified K-means algorithm has been proposed where the codevector updating step is as fallows: new codevector = current codevector + scale factor (new centroid - current codevector). This algorithm uses a fixed value for the scale factor. In this paper, we propose a new algorithm for the enhancement of learning time in fuzzy c-means a1gorithm. Experimental results show that the proposed method produces codebooks about 5 to 6 times faster than the conventional K-means algorithm with almost the same Performance.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.312-323
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• 2014

Voice activity detection is very important process that find voice activity from noisy speech signal for noise cancelling and speech enhancement. Over the past few years, many studies have been made on voice activity detection, it has poor performance for speech signal of sentence form in a low SNR environment. In this paper, it proposed new voice activity detection algorithm that has beginning VAD process using entropy and main VAD process using fuzzy membership shifted c-means clustering. We conduct an experiment in various SNR environment of white noise to evaluate performance of the proposed algorithm and confirmed good performance of the proposed algorithm.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.609-617
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• 2006

A new fuzzy c-regression algorithm for switching regression analysis is presented, which combines fuzzy c-means clustering and least squares support vector machine. This algorithm can detect outliers in switching regression models while yielding the simultaneous estimates of the associated parameters together with a fuzzy c-partitions of data. It can be employed for the model-free nonlinear regression which does not assume the underlying form of the regression function. We illustrate the new approach with some numerical examples that show how it can be used to fit switching regression models to almost all types of mixed data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.