• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.387-397
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• 2003

Generally, most of researches that need a variable-clustering process use an exploratory factor analysis technique or a divisive hierarchical variable-clustering method based on a correlation matrix. And some researchers apply a object-clustering method to a distance matrix transformed from a correlation matrix, though this approach is known to be improper. On this paper an agglomerative hierarchical variable-clustering method based on a correlation matrix itself is suggested. It is derived from a geometric concept by using variate-spaces and a characterizing variate.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.549-556
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• 2005

A hard-type centroids in the conventional clustering algorithm such as k-modes algorithm cannot keep the uncertainty inherently in data sets as long as possible before actual clustering(decision) are made. Therefore, we propose the k-populations algorithm to extend clustering ability and to heed the data characteristics. This k-population algorithm as found to give markedly better clustering results through various experiments.

• 한국경영과학회지
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• 제14권1호
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• pp.108-118
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• 1989

The clustering problem for multi-dimensional entities is investigated. A heuristic method, which is named as Difference Order Clustering (DOC) is developed for the grouping of multi-dimensional entities DOC method has an advantage of identifying the bottle-neck entities. Comparisons among the proposed DOC method, modified rank order clustering (MODROC) method, and lexicographical rank order clustering using minimum spanning tree (lexico-MMSTROC) are illustrated by a part type selection problems.

• 산업경영시스템학회지
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• 제26권1호
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• pp.17-21
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• 2003

We study clustering algorithm for sequences of categorical values. Clustering is a data mining problem that has received significant attention by the database community. Traditional clustering algorithms deal with numerical or categorical data points. However, there exist many important databases that store categorical data sequences. In this paper, we introduce new similarity measure and develop a hierarchical clustering algorithm. An experimental section shows performance of the proposed approach.

• Journal of Korean Neurosurgical Society
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• 제52권5호
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• pp.466-471
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• 2012

Objective : The aim of this study was to devise an objective clustering method for magnetoencephalography (MEG) interictal spike sources, and to identify the prognostic value of the new clustering method in adult epilepsy patients with cortical dysplasia (CD). Methods : We retrospectively analyzed 25 adult patients with histologically proven CD, who underwent MEG examination and surgical resection for intractable epilepsy. The mean postoperative follow-up period was 3.1 years. A hierarchical clustering method was adopted for MEG interictal spike source clustering. Clustered sources were then tested for their prognostic value toward surgical outcome. Results : Postoperative seizure outcome was Engel class I in 6 (24%), class II in 3 (12%), class III in 12 (48%), and class IV in 4 (16%) patients. With respect to MEG spike clustering, 12 of 25 (48%) patients showed 1 cluster, 2 (8%) showed 2 or more clusters within the same lobe, 10 (40%) showed 2 or more clusters in a different lobe, and 1 (4%) patient had only scattered spikes with no clustering. Patients who showed focal clustering achieved better surgical outcome than distributed cases (p=0.017). Conclusion : This is the first study that introduces an objective method to classify the distribution of MEG interictal spike sources. By using a hierarchical clustering method, we found that the presence of focal clustered spikes predicts a better postoperative outcome in epilepsy patients with CD.

• 예술인문사회 융합 멀티미디어 논문지
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• 제7권8호
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• pp.305-314
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• 2017

Clustering is a method that collects data objects into groups based on their similary. Performance of the state-of-the-art clustering methods is different according to the data characteristics. There have been numerous studies that performed experiments to compare the accuracy of the state-of-the-art clustering methods by applying various kinds of datasets. A common problem of these studies is that they only consider clustering algorithms that yield the most accurate results for a particular dataset. They do not consider what factors affect the execution time of each clustering method and how they are affected. Nevertheless, execution time is an important factor in clustering performance if there is no significant difference in accuracy. In order to solve the problems of the existing research, through a series of experiments using various types of datasets, we compare the accuracy of four representative clustering methods. In addition, we perform practical clustering performance comparisons by deriving time complexity and identifying factors that influences to its performance.

• 한국경영과학회지
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• 제31권2호
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• pp.87-98
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• 2006

In many cases of cluster analysis we are forced to perform clustering without any prior knowledge on the number of clusters. But in some clustering methods such as k-means algorithm it is required to provide the number of clusters beforehand. In this study, we focus on the problem to determine the number of clusters in the given data. We follow the 2 stage approach of ASA clustering algorithm and mainly try to improve the performance of the first stage of the algorithm. We verify the usefulness of the method by applying it for various kinds of simulated data. Also, we apply the method for clustering two kinds of real life qualitative data.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1989년도 추계학술발표회 발표논문초록집; 이화여자대학교, 서울; 23 Sep. 1989
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• pp.30-39
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• 1989

일반적으로 cluster의 수가 미정인 상황하에서의 clustering문제는 semistructured문제로 알려져 있다. clustering문제를 구조화하는데 있어서 해의 품질평가(evaluation of solution quality)가 필수적이나 각 응용분야에 널리 적용될 수 있는 척도는 아직까지 개발되어있지 못한 상태이다. 그 주된 원인은 cluster해에 대한 개념적 차원에서의 평가기준은 제시되어있으나 척도의 구현에 있어서는 제시된 개념들이 명확하게 적용될 정도의 수준으로는 구체화되지 못한데에 기인한다고 할 수 있다. 본 연구의 목적은 개체차원이 다차원으로 확장된 clustering문제를 대상으로하는 clustering문제의 척도개발에 있다.

• 반도체디스플레이기술학회지
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• 제13권3호
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• pp.1-6
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• 2014

Visual defect inspection for electronics parts manufacturing processes is comprised of 2 steps - automatic visual inspection by machine and inspection by human inspectors. It is necessary that spatial points which were detected by the machine should be adequately clustered for subsequent human inspection. This research deals with the spatial clustering algorithm for the purpose of process productivity improvement. Distribution based clustering is newly developed and experimentally confirmed to show better clustering efficiency than existing algorithm - area based clustering.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.89-93
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• 2010

In this paper a new fuzzy clustering approach to the color clustering problem has been proposed. To deal with the limitations of the traditional FCM algorithm, we propose a spatial homogeneity-based FCM algorithm. Moreover, the cluster validity index is employed to automatically determine the number of clusters for a given image. We refer to this method as VS-FCM algorithm. The effectiveness of the proposed method is demonstrated through various clustering examples.