• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.9-11
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• 2005

We propose a optimal identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally identity we use genetic algorithm (GAs) sand Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the selected input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data 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(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.262-267
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• 2008

The focus of this paper is on devising an efficient clustering task for arbitrary color data. In order to tackle this problem, the inherent uncertainty and vagueness of color are represented by a fuzzy color model. By taking a fuzzy approach to color representation, the proposed model makes a soft decision for the vague regions between neighboring colors. A definition on a three-dimensional fuzzy color ball is introduced, and the degree of membership of color is computed by employing a distance measure between a fuzzy color and color data. With the fuzzy color model, a novel fuzzy clustering algorithm for efficient partition of color data is developed.

• Nuclear Engineering and Technology
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• v.40 no.1
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• pp.69-76
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• 2008

Most pressurized water reactors use Venturi flow meters to measure the feedwater flow rate. However, fouling phenomena, which allow corrosion products to accumulate and increase the differential pressure across the Venturi flow meter, can result in an overestimation of the flow rate. In this study, a soft-sensing model based on fuzzy support vector regression was developed to enable accurate on-line prediction of the feedwater flow rate. The available data was divided into two groups by fuzzy c means clustering in order to reduce the training time. The data for training the soft-sensing model was selected from each data group with the aid of a subtractive clustering scheme because informative data increases the learning effect. The proposed soft-sensing model was confirmed with the real plant data of Yonggwang Nuclear Power Plant Unit 3. The root mean square error and relative maximum error of the model were quite small. Hence, this model can be used to validate and monitor existing hardware feedwater flow meters.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.715-723
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• 2002

This paper is to show a clustering application of a density estimation method that utilizes the Gaussian mixture model. We define "closeness measure" as a clustering criterion to see how close given two Gaussian components are. Closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold valuesold values

• Phonetics and Speech Sciences
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• v.6 no.1
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• pp.77-83
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• 2014

This paper proposes a speaker and environment clustering method in order to overcome the degradation of the speech recognition performance caused by various noise and speaker characteristics. In this paper, instead of using the distance between Gaussian mixture model (GMM) weight vectors as in the Google's approach, the distance between the adapted mean vectors based on the modified maximum a posteriori (MAP) adaptation is used as a distance measure for vector quantization (VQ) clustering. According to our experiments on the simulation data generated by adding noise to clean speech, the proposed clustering method yields error rate reduction of 10.6% compared with baseline speaker-independent (SI) model, which is slightly better performance than the Google's approach.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.142-151
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• 2009

Previous document clustering method, NSTC measures similarities between two document pairs using TF-IDF during web document clustering. In this paper, we propose new similarity measure using common phrase-based relational graph, not TF-IDF. This method suggests that weighting common phrases by relational graph presenting relationship among common phrases in document collection. And experimental results indicate that proposed method is more effective in clustering document collection than NSTC.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.410-411
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• 2006

This paper is focused on the survey on the power system modeling using a clustering algorithm. In electricity markets, clustering method is a efficient tool to model the power system. It can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be widely applicable to other technical problems in power system such as generation scheduling, power flow analysis, short-term load forecasting, and so on. There are several researches on the power system modeling using a clustering algorithm. We specially surveyed their own clustering methods to model the power system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.536-543
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• 2004

In this paper, we proposed a new method of modeling a neuro-fuzzy system using a hybrid clustering algorithm. The initial parameters and the number of clusters of the proposed system are optimally chosen simultaneously with respect to the process of regression, which is a unique characteristics of the proposed system. The proposed algorithm presented in this work improves the overall performance of the proposed a neuro-fuzzy system by choosing a proper number of clusters adaptively according the characteristics of given data. The process of clustering is performed by deciding on the number of classes, which yields the property of convergence of the system. In experiments, the superiority of the proposed neuro-fuzzy system is demonstrated, especially the process of optimizing parameters and clustering of learning speed.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.741-753
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• 2011

We accomplish clustering analyses for yeast cell cycle microarray expression data. We compare model-based clustering, K-means, PAM, SOM and hierarchical Ward method with yeast data. As the validity measure for clustering results, connectivity, Dunn Index and silhouette values are computed and compared.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.179-184
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• 2013

Traditional clustering methods are usually based on the bag-of-words (BOW) model. A disadvantage of the BOW model is that it ignores the semantic relationship among terms in the data set. To resolve this problem, ontology or matrix factorization approaches are usually used. However, a major problem of the ontology approach is that it is usually difficult to find a comprehensive ontology that can cover all the concepts mentioned in a collection. This paper proposes a new document clustering method using semantic features and fuzzy relations for solving the problems of ontology and matrix factorization approaches. The proposed method can improve the quality of document clustering because the clustered documents use fuzzy relation values between semantic features and terms to distinguish clearly among dissimilar documents in clusters. The selected cluster label terms can represent the inherent structure of a document set better by using semantic features based on non-negative matrix factorization, which is used in document clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.