그리드 기반 표본의 무게중심을 이용한 클러스터링 알고리즘 개발
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Cluster analysis has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on on-line or off-line and so on. Clustering can identify dense and sparse regions among data attributes or object attributes. But it requires many hours to get clusters that we want, because of clustering is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new clustering method, 'Clustering algorithm using a center of gravity for grid-based sample'. It is more fast than any traditional clustering method and maintains it's accuracy. It reduces running time by using grid-based sample and keeps accuracy by using representative points, a center of gravity. And other clustering applications can be more effective by using this methods with its' original methods.