• Journal of the Korea Computer Graphics Society
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• v.15 no.3
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• pp.19-26
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• 2009

In this paper, a new adaptive vertex clustering using a KD-tree is presented for 3D mesh partitioning. A vertex clustering is used to divide a huge 3D mesh into several partitions for various mesh processing. An octree-based clustering and K-means clustering are currently leading techniques. However, the octree-based methods practice uniform space divisions and so each partitioned mesh has non-uniformly distributed number of vertices and the difference in its size. The K-means clustering produces uniformly partitioned meshes but takes much time due to many repetitions and optimizations. Therefore, we propose to use a KD-tree to efficiently partition meshes with uniform number of vertices. The bounding box region of the given mesh is adaptively subdivided according to the number of vertices included and dynamically determined axis. As a result, the partitioned meshes have a property of compactness with uniformly distributed vertices.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• Journal of the Korean Operations Research and Management Science Society
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• v.42 no.3
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• pp.25-34
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• 2017

K-means is a popular and efficient data clustering method that only uses intra-cluster distance to establish a valid index with a previously fixed number of clusters. K-means is useless without a suitable number of clusters for unsupervised data. This paper aimsto propose the Group Search Optimization (GSO) using Silhouette to find the optimal data clustering solution with a number of clusters for unsupervised data. Silhouette can be used as valid index to decide the number of clusters and optimal solution by simultaneously considering intra- and inter-cluster distances. The performance of GSO using Silhouette is validated through several experiment and analysis of data sets.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.719-732
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• 2006

A set of clustering algorithms with proper weight on the formulation of distance which extend to mixed numeric and multiple binary values is presented. A simple matching and Jaccard coefficients are used to measure similarity between objects for multiple binary attributes. Similarities are converted to dissimilarities between i th and j th objects. The performance of clustering algorithms with balancing weight on different similarity measures is demonstrated. Our experiments show that clustering algorithms with application of proper weight give competitive recovery level when a set of data with mixed numeric and multiple binary attributes is clustered.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.993-997
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• 2009

Clustering is well known method and that can be used in statistical machine translation. In this paper we propose a corpus clustering method using syntactic structure and POS information of dependency grammar. And using this cluster language model as additional feature to phrased-based statistical machine translation system to improve translation Quality.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.421-426
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• 2006

We propose a new algorithm for K-medoids clustering which runs like the K-means clustering algorithm and test several methods for selecting initial medoids. The proposed algorithm calculates similarity matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm we use real and artificial data and compare with the clustering results of other algorithms in terms of three performance measures. Experimental results show that the proposed algorithm takes the reduced time in computation with comparable performance as compared to the Partitioning Around Medoids.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.973-974
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• 2008

As radar signal environments become denser and more complex, the capability of high-speed and accurate signal analysis is required for ES(Electronic warfare Support) system to identify individual radar signals at real-time. In this paper, we propose the new novel clustering algorithm of radar pulses to alleviate the load of signal analysis process and support reliable analysis. The proposed algorithm uses KDE(Kernel Density Estimation) and its CDF(Cumulative Distribution Function) to compose clusters considering the distribution characteristics of pulses. Simulation results show the good performance of the proposed clustering algorithm in clustering and classifying the emitters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5437-5454
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• 2016

A Wireless Sensor Network (WSN) is composed of several sensor nodes which are severely restricted to energy and memory. Energy is the lifeblood of sensors and thus energy conservation is a critical necessity of WSN. This paper proposes a clustering algorithm namely Energy Efficient Trustworthy Clustering algorithm (EETCA), which focuses on three phases such as chief node election, chief node recycling process and bi-level trust computation. The chief node election is achieved by Dempster-Shafer theory based on trust. In the second phase, the selected chief node is recycled with respect to the current available energy. The final phase is concerned with the computation of bi-level trust, which is triggered for every time interval. This is to check the trustworthiness of the participating nodes. The nodes below the fixed trust threshold are blocked, so as to ensure trustworthiness. The system consumes lesser energy, as all the nodes behave normally and unwanted energy consumption is completely weeded out. The experimental results of EETCA are satisfactory in terms of reduced energy consumption and prolonged lifetime of the network.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.244-248
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• 2012

F-Measure is one of the external measures for evaluating the validity of clustering results. Though it has clear advantages over other widely used external measures such as Purity and Entropy, F-Measure has inherently been less sensitive than other validity measures. This insensitivity owes to the definition of F-Measure that counts only most influential portions. In this research, we present $F_n$-Measure, an external cluster evaluation measure based on F-Measure. $F_n$-Measure is so sensitive that it can detect their difference in the cases that F-Measure cannot detect the difference in clustering results. We compare $F_n$-Measure to F-Measure for a few clustering results and show which measure draws better result based upon homogeneity and completeness.

• The Journal of the Acoustical Society of Korea
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• v.6 no.4
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• pp.44-54
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• 1987

In this paper, the problem of LP-model and it's solution by liftering in cepstrum domain are investigated in speaker independent isolated-word recognition. And, clustering technique is discussed for obtaining the reference template. KMA (K-means iteration with average) method, which is transformed from UWA method and K-iteration method, has been suggested and compared with each other for clustering, the result of recognition experiments shows max. $95\%$ recognition rate when rasied-sign lifter and KMA clustering method is applied.