• 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.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.169-179
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• 2021

This study shows that the two-stage k-means clustering method can improve prediction performance by predicting the stock price, To this end, this study introduces the two-stage k-means clustering algorithm and tests the prediction performance through comparison with various machine learning techniques. It selects the cluster close to the prediction target obtained from the k-means clustering, and reapplies the k-means clustering method to the cluster to search for a cluster closer to the actual value. As a result, the predicted value of this method is shown to be closer to the actual stock price than the predicted values of other machine learning techniques. Furthermore, it shows a relatively stable predicted value despite the use of a relatively small cluster. Accordingly, this method can simultaneously improve the accuracy and stability of prediction, and it can be considered as the new clustering method useful for small data. In the future, developing the two-stage k-means clustering is required for the large-scale data application.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.319-326
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• 2002

In this paper, we have designed and implemented cluttering classification systems- unsupervised classifiers-for the processing of satellite remote sensing images. Implemented systems adopt various design patterns which include a factory pattern and a strategy pattern to support various satellite images'formats and to design compatible systems. The clustering systems consist of sequential clustering, K-Means clustering, ISODATA clustering and Fuzzy C-Means clustering classifiers. The systems are tested by using a Landsat TM satellite image for the classification input. As results, these clustering systems are well designed to extract sample data for the classification of satellite images of which there is no previous knowledge. The systems can be provided with real-time base clustering tools, compatibilities and components' reusabilities as well.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.103-111
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• 2011

Clustering methods is divided into hierarchical clustering, partitioning clustering, and more. If the amount of documents is huge, it takes too much time to cluster them in hierarchical clustering. In this paper we deal with K-Means algorithm that is one of partitioning clustering and is adequate to cluster so many documents rapidly and easily. We propose the new method of selecting initial seeds in K-Means algorithm. In this method, the initial seeds have been selected that are positioned as far away from each other as possible.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.83-88
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• 2013

This paper proposes a recommending system of the similar blogs gathered with similarities between blogs according to the similarity, dividing words, for each frequency, that individual blogs have. It improved the algorithm of k-means, using the model of the habits of white ants for better performance of clustering, and showed better performance of clustering as a result of evaluating and comparing with the existing algorithm of k-means as the improved algorithm. The recommending system of similar blog was designed and embodied, using the improved algorithm. TCA can reduce clustering time and the number of moving time for clustering compare with K-means algorithm.

• Journal of Internet Computing and Services
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• v.13 no.6
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• pp.1-8
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• 2012

Clustering method is divided into hierarchical clustering, partitioning clustering, and more. K-Means algorithm is one of partitioning clustering and is adequate to cluster so many documents rapidly and easily. It has disadvantage that the random initial centers cause different result. So, the better choice is to place them as far away as possible from each other. We propose a new method of selecting initial centers in K-Means clustering. This method uses triangle height for initial centers of clusters. After that, the centers are distributed evenly and that result is more accurate than initial cluster centers selected random. It is time-consuming, but can reduce total clustering time by minimizing the number of allocation and recalculation. We can reduce the time spent on total clustering. Compared with the standard algorithm, average consuming time is reduced 38.4%.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.107-110
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• 2021

글로벌 퍼블릭 클라우드 산업 규모는 매년 폭발적으로 성장하고 있으며 최근 COVID-19 등 비대면 문화 확산에 따라 지속 확장되고 있다. 클라우드 사업자는 유한한 인프라 자원으로 다수의 사용자에게 양질의 IT 서비스 제공을 위해 잉여 자원 할당을 최소화하는 것이 중요하다. 그러나 일반적인 퍼블릭 클라우드 환경에서는 정적 자원 할당 기법을 채택하고 있기 때문에 사용자의 주관적인 판단에 따라 잉여 자원의 발생은 필연적이다. 본 논문에서는 머신 러닝 기법 중 K-Means Clustering 알고리즘을 적용하여 클라우드 동적 자원 관리 기법을 제안한다. K-Means Clustering 기반으로 클라우드에 탑재된 각 Instance 의 자원 사용률 데이터를 분석하고, 분석 결과를 토대로 각 Instance 가 속한 Cluster 에 대하여 자원 최적화 작업을 수행한다. 이를 통해 전체 데이터센터 관점에서 잉여 자원의 발생을 최소화하면서도 SLA 수준 및 서비스 연속성을 보장한다.

• 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 Institute of Intelligent Systems
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• v.18 no.3
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• pp.354-359
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• 2008

Clustering is a method to classify the given data set with same property into several classes. To cluster data, many methods such as K-Means, Fuzzy C-Means(FCM), Mountain Method(MM), and etc, have been proposed and used. But the clustering results of conventional methods are sensitively influenced by initial values given for clustering in each method. Especially, FCM is very sensitive to noisy data, and cluster center distortion phenomenon is occurred because the method dose clustering through minimization of within-clusters variance. In this paper, we propose a clustering method which reduces cluster center distortion through merging the nearest data based on the data weight, and not being influenced by initial values. We show the effectiveness of the proposed through experimental results applied it to various types of data sets, and comparison of cluster centers with those of FCM.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.447-455
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• 2000

We propose a weighted kernel pattern recognition method using the K -means clustering algorithm to reduce computation and storage required for the full kernel classifier. This technique finds a set of reference vectors and weights which are used to approximate the kernel classifier. Since the hierarchical clustering method implemented in the 'Weighted Parzen Window (WP\V) classifier is not able to rearrange the proper clusters, we adopt the K -means algorithm to find reference vectors and weights from the more properly rearranged clusters \Ve find that the proposed method outperforms the \VP\V method for the repre~entativeness of the reference vectors and the data reduction.