• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.95-101
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.4
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• pp.179-192
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• 2004

In this paper, we present that the result of document clustering can change dramatically with respect to the different ways of indexing compound nouns. First of all, the automatic indexing engine specialized for Korean words analysis, which also serves as the backbone engine for automatic document clustering system, is introduced. Then, the details of hierarchical agglomerative clustering(HAC) method, one of the widely used clustering methodologies in these days, was illustrated. As the result of observing the experiments, carried out in the final part of this paper, it comes to the conclusion that the various modes of indexing compound nouns have an effect on the outcome of HAC.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.490-496
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• 2005

In this paper, we propose a self-constructed clustering algorithm based on inference information of the fuzzy model. This method makes it possible to automatically detect and optimize the number of cluster and parameters by using input-output data. The propose method improves the performance of clustering by extended supervised learning technique. This technique uses the output information as well as input characteristics. For effect the similarity measure in clustering, we use the TSK fuzzy model to sent the information of output. In the conceptually, we design a learning method that use to feedback the information of output to the clustering since proposed algorithm perform to separate each classes in input data space. We show effectiveness of proposed method using simulation than previous ones

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.465-468
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• 2008

Clustering techniques in wireless sensor networks is developed to minimize the energy consumption of node, show the effect that increases the network lifetime. Existing clustering techniques proposed the method that increases the network lifetime equalizing each node's the energy consumption by rotating the role of CH(Cluster Head), but these algorithm did not present the resolution that minimizes the energy consumption of neighboring nodes with sink. In this paper, we propose the clustering algorithm that prolongs the network lifetime by not including a part of nodes in POS(Personal Operating Space) of the sink in a cluster and communicating with sink directly to reduce the energy consumption of CH closed to sink.

• Earthquakes and Structures
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• v.23 no.6
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• pp.549-580
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• 2022

This paper assesses the influences of modeling assumptions and uncertainties on the performance of the non-linear finite element (FE) model updating procedure and model clustering method. The results of a shaking table test on a four-story steel moment-resisting frame are employed for both calibrations and clustering of the FE models. In the first part, simple to detailed non-linear FE models of the test frame is calibrated to minimize the difference between the various data features of the models and the structure. To investigate the effect of the specified data feature, four of which include the acceleration, displacement, hysteretic energy, and instantaneous features of responses, have been considered. In the last part of the work, a model-based clustering approach to group models of a four-story frame with similar behavior is introduced to detect abnormal ones. The approach is a composition of property derivation, outlier removal based on k-Nearest neighbors, and a K-means clustering approach using specified data features. The clustering results showed correlations among similar models. Moreover, it also helped to detect the best strategy for modeling different structural components.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.1-7
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• 2020

Fuzzy clustering, represented by FCM(Fuzzy C-Means), is a simple and efficient clustering method. However, the object function in FCM makes clusters affect clustering results proportional to the density of clusters, which can distort clustering results due to density difference between clusters. One method to alleviate this density problem is EDI-FCM(Extended Density-Independent FCM), which adds additional terms to the objective function of FCM to compensate for the density difference. In this paper, proposed is an enhanced EDI-FCM using regularization, Regularized EDI-FCM. Regularization is commonly used to make a solution space smooth and an algorithm noise insensitive. In clustering, regularization can reduce the effect of a high-density cluster on clustering results. The proposed method converges quickly and accurately to real centers when compared with FCM and EDI-FCM, which can be verified with experimental results.

• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.3-8
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• 2009

In an effort to improve speech recognition, we investigated the effect of the number of clusters. In usual LBG clustering, the number of codebook clusters is doubled on each bifurcation and hence cannot be chosen arbitrarily in a natural way. To have the number of clusters at our control, we combined adaptive resonance theory (ART2) with LBG and perform the clustering in two stages. The codebook thus formed was used in subsequent processing of fuzzy vector quantization (FVQ) and HMM for speech recognition tests. Compared to conventional LBG, our method was shown to reduce the best recognition error rate by 0${\sim$}0.9% depending on the vocabulary size. The result also showed that between 400 and 800 would be the optimal number of clusters in the limit of small and large vocabulary speech recognitions of isolated words, respectively.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.415-422
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• 2012

For statistical microarray data analysis, clustering analysis is a useful exploratory technique and offers the promise of simultaneously studying the variation of many genes. However, most of the proposed clustering methods are not rigorously solved for a time-course microarray data cluster and for a fitting time covariate; therefore, a statistical method is needed to form a cluster and represent a linear trend of each cluster for each gene. In this research, we developed a modified hierarchical mixture of an experts model to suggest clustering data and characterize each cluster using a linear mixed effect model. The feasibility of the proposed method is illustrated by an application to the human fibroblast data suggested by Iyer et al. (1999).

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.138-140
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• 1992

This paper represents how to reduce the computer time in small signal stability analysis by selecting the dominant oscillation modes with frequency of 0.5 to 1.2 Hz using the clustering technique. Clustering technique links the buses which are expected to be similar with zero-impedance lines and the voltage variations of these buses are regarded to be identical. The computer time was reduced remarkably with this technique and the effect of clustering will be powerful in the analysis of large-scale power systems.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.11-15
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• 2018

The application of Wireless Sensor Networks requires a wise utilization of limited energy resources. Therefore, a wide range of routing protocols with a motivation to prolong the lifetime of a network has been proposed in recent years. Hierarchical clustering based protocols have become an object of a large number of studies that aim to efficiently utilize the limited energy of network components. In this paper, the effect of mismatch in parameter estimation is discussed to evaluate the robustness of a distanced based algorithm called distributed clustering protocol in homogeneous and heterogeneous environment. For quantitative analysis, performance simulations for this protocol are carried out in terms of the network lifetime which is the main criteria of efficiency for the energy limited system.