• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.577-586
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• 2005

Many algorithms have been proposed for (ace recognition that is one of the most successful applications in image processing, pattern recognition and computer vision fields. Research for what kind of attribute of face that make harder or easier recognizing the target is going on recently. In flus paper, we propose method to improve recognition performance using relevance of face data and applied algorithms, because recognition performance of each algorithm according to facial attribute(illumination and expression) is change. In the experiment, we use n-tuple classifier, PCA and Gabor wavelet as recognition algorithm. And we propose three vectorization methods. First of all, we estimate the fitnesses of three recognition algorithms about each cluster after clustering the test data using k-means algorithm then we compose new clusters by integrating clusters that select same algorithm. We estimate similarity about a new cluster of test data and then we recognize the target using the nearest cluster. As a result, we can observe that the recognition performance has improved than the performance by a single algorithm without clustering.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.4
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• pp.343-350
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• 2014

Divisive hierarchical clustering algorithms iterate the process of decomposition and clustering data recursively. In each recursive call, data in each cluster are arbitrarily selected and thus, the total clustering time can be increased, which causes a problem that it is difficult to apply the process of clustering neighbor vehicular position data in vehicular localization. In this paper, we propose a new heuristic algorithm for speeding up the clustering time by eliminating randomness of the selected data in the process of generating the initial divisive clusters.

• Journal of the Korean Society for information Management
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• v.18 no.2
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• pp.203-230
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• 2001

The purpose of this study is to develop a document clustering model for automatic classification of knowledge. Two test collections of newspaper article texts and journal article abstracts are built for the clustering experiment. Various feature reduction criteria as well as term weighting methods are applied to the term sets of the test collections, and cosine and Jaccard coefficients are used as similarity measures. The performances of complete linkage and K-means clustering algorithms are compared using different feature selection methods and various term weights. It was found that complete linkage clustering outperforms K-means algorithm and feature reduction up to almost 10% of the total feature sets does not lower the performance of document clustering to any significant extent.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.969-977
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• 2021

We cluster the electricity consumption of households in A-apartment in Seoul, Korea using Hierarchical K-means clustering algorithm. The data is recorded from the advanced metering infrastructure (AMI), and we focus on the electricity consumption during evening weekdays in summer. Compare to the conventional clustering algorithms, Hierarchical K-means clustering algorithm is recently applied to the electricity usage data, and it can identify usage patterns while reducing dimension. We apply Hierarchical K-means algorithm to the AMI data, and compare the results based on the various clustering validity indexes. The results show that the electricity usage patterns are well-identified, and it is expected to be utilized as a major basis for future applications in various fields.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.143-152
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• 2022

The ongoing adoption of 5G will increase the data traffic, throughput, multimedia services, and power consumption for future wireless applications and services, including sensor and mobile networks. Multipath fading on wireless channels also reduces the system performance and increases energy consumption. To address these issues, device-to-device (D2D) and cooperative communications have been proposed. In this study, we propose two inter-clustering models using the relay selection method to improve system performance and increase energy efficiency in cooperative D2D networks. We develop two inter-clustering models and present their respective algorithms. Subsequently, we run a computer simulation to evaluate each model's outage probability (OP) performance, throughput, and energy efficiency. The simulation results show that inter-clustering model II has the lowest OP, highest throughput, and highest energy efficiency compared with inter-clustering model I and the conventional inter-clustering-based multirelay method. These results demonstrate that inter-clustering model II is well-suited for use in 5G overlay D2D and cellular communications.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.977.1-1000
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• 1993

This paper presents a fuzzy neural network, called the fuzzy counterpropagation network, that structures its inputs and generates its outputs in a manner based on counterpropagation networks. The fuzzy counterpropagation network is developed by incorporating the concept of fuzzy clustering into the hidden layer responses. Three learning algorithms are introduced for use with the proposed network. Simulations demonstrate that fuzzy counterpropagation networks with the proposed learning algorithms work well on approximating bipolar and continuous functions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3512-3528
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• 2016

A network with high mobility nodes or vehicles is vehicular ad hoc Network (VANET). For improvement in communication efficiency of VANET, many techniques have been proposed; one of these techniques is vehicular node clustering. Cluster nodes (CNs) and Cluster Heads (CHs) are elected or selected in the process of clustering. The longer the lifetime of clusters and the lesser the number of CHs attributes to efficient networking in VANETs. In this paper, a novel Clustering algorithm is proposed based on Ant Colony Optimization (ACO) for VANET named ACONET. This algorithm forms optimized clusters to offer robust communication for VANETs. For optimized clustering, parameters of transmission range, direction, speed of the nodes and load balance factor (LBF) are considered. The ACONET is compared empirically with state of the art methods, including Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering techniques. An extensive set of experiments is performed by varying the grid size of the network, the transmission range of nodes, and total number of nodes in network to evaluate the effectiveness of the algorithms in comparison. The results indicate that the ACONET has significantly outperformed the competitors.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.293-301
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• 2014

In this paper, we propose a new measure based on the confidence of Bayesian posterior probability so as to reduce unimportant information in the clustering process. Because the performance of clustering is up to selecting the important degree of attributes within the databases, the concept of information entropy is added to posterior probability for attributes discernibility. Hence, The same value of attributes in the confidence of the proposed measure is considerably much less due to the natural logarithm. Therefore posterior probability-based clustering algorithm selects the minimum of attribute reducts and improves the efficiency of clustering. Analysis of the validation of the proposed algorithms compared with others shows their discernibility as well as ability of clustering to handle uncertainty with ACME categorical data.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.555-576
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• 2016

In the conventional clustering algorithms, an object could be assigned to only one group. However, this is sometimes not the case in reality, there are cases where the data do not belong to one group. As against, the fuzzy clustering takes into consideration the degree of fuzzy membership of each pixel relative to different classes. In order to overcome some shortcoming with traditional clustering methods, such as slow convergence and their sensitivity to initialization values, we have used the Harmony Search algorithm. It is based on the population metaheuristic algorithm, imitating the musical improvisation process. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. We propose in this paper a new unsupervised clustering method called the Fuzzy Harmony Search-Fourier Transform (FHS-FT). It is based on hybridization fuzzy clustering and the harmony search algorithm to increase its exploitation process and to further improve the generated solution, while the Fourier transform to increase the size of the image's data. The results show that the proposed method is able to provide viable solutions as compared to previous work.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.