• Smart Structures and Systems
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• v.8 no.1
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• pp.69-92
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• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.189-196
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• 2014

Recently, the need for retrieving documents is growing in SNS environment such as twitter. For supporting the twitter search, a clustering technique classifying the massively retrieved documents in terms of topics is required. However, due to the nature of twitter, there is a limit in applying previous simple techniques to clustering the twitter documents. To overcome such problem, we propose in this paper a new clustering technique suitable to twitter environment. In proposed method, we augment new terms to feature vectors representing the twitter documents, and recalculate the weights of features using Korean Wikipedia. In addition, we performed the experiments with Korean twitter documents, and proved the usability of proposed method through performance comparison with the previous techniques.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.151-166
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• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.512-519
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• 2003

In this paper, we propose a neuro-fuzzy modeling to improve the performance using the hierarchical clustering and Gaussian Mixture Model(GMM). The hierarchical clustering algorithm has a property of producing unique parameters for the given data because it does not use the object function to perform the clustering. After optimizing the obtained parameters using the GMM, we apply them as initial parameters for Adaptive Network-based Fuzzy Inference System. Here, the number of fuzzy rules becomes to the cluster numbers. From this, we can improve the performance index and reduce the number of rules simultaneously. The proposed method is verified by applying to a neuro-fuzzy modeling for Box-Jenkins s gas furnace data and Sugeno's nonlinear system, which yields better results than previous oiles.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.

• Publications of The Korean Astronomical Society
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• v.27 no.4
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• pp.343-344
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• 2012

We present the first measurement of the angular two-point correlation function for AKARI $90{\mu}m$ point sources, detected outside of the Milky Way plane and selected as candidates for extragalactic sources. This is the first measurement of the large-scale angular clustering of galaxies selected in the far-infrared after IRAS. We find a positive clustering signal in both hemispheres extending up to ~ 40 degrees, without any significant fluctuations at larger scales. The observed correlation function is well fitted by a power law function. However, southern galaxies seem to be more strongly clustered than northern ones and the difference is statistically significant. The reason for this difference - technical or physical - is still to be found.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.427-436
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• 1999

This paper describes an ECG signal labeling algorithm based on fuzzy clustering, which is very useful to the automated ECG diagnosis. The existing labeling methods compares the crosscorrelations of each wave form using IF-THEN binary logic, which tends to recognize the same wave forms such as different things when the wave forms have a little morphological variation. To prevent this error, we have proposed as ECG signal labeling algorithm using fuzzy clustering. The center and the membership function of a cluster is calculated by a cluster validity function. The dominant cluster type is determined by RR interval, and the representative beat of each cluster is determined by MF (Membership Function). The problem of IF-THEN binary logic is solved by FCM (Fuzzy C-Means). The MF and the result of FCM can be effectively used in the automated fuzzy inference -ECG diagnosis.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.925-936
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• 2008

An association rule mining finds the relation among each items in massive volume database. In generating association rules, the researcher specifies the measurements randomly such as support, confidence and lift, and produces the rules. The rule is not produced if it is not suitable to the one any condition which is given value. For example, in case of a little small one than the value which a confidence value is specified but a support and lift's value is very high, this rule is meaningful rule. But association rule mining can not produce the meaningful rules in this case because it is not suitable to a given condition. Consequently, we creat insignificant error which is not selected to the meaningful rules. In this paper, we suggest clustering technique to association rule measures for finding effective association rules using measure of association.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.27-30
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• 2005

Because of unreplenishable power resources, reducing node energy consumption to extend network lifetime is an important requirement in wireless sensor networks. In addition both path length and path cost are important metrics affecting sensor lifetime. We propose a dynamic clustering scheme based on location in wireless sensor networks. Our scheme can localize the effects of route failures, reduce control traffic overhead, and thus enhance the reachability to the destination. We have evaluated the performance of our clustering scheme through a simulation and analysis. We provide simulation results showing a good performance in terms of approximation ratios.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2098-2106
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• 2014

In the area of clustering, there are numerous approaches to construct clusters in the input space. For regression problem, when forming clusters being a part of the overall model, the relationships between the input space and the output space are essential and have to be taken into consideration. Conditional Fuzzy C-Means (c-FCM) clustering offers an opportunity to analyze the structure in the input space with the mechanism of supervision implied by the distribution of data present in the output space. However, like other clustering methods, c-FCM focuses on the distribution of the data. In this paper, we introduce a new method, which by making use of the ambiguity index focuses on the boundaries of the clusters whose determination is essential to the quality of the ensuing classification procedures. The introduced design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the fuzzy classifiers and quantify several essentials design aspects.