한국데이터정보과학회:학술대회논문집
- 한국데이터정보과학회 2003년도 추계학술대회
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- Pages.249-258
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- 2003
K-means Clustering using a Grid-based Sampling
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Park, Hee-Chang
(Department of Statistics, Changwon National University) ;
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Lee, Sun-Myung
(Department of Statistics, Changwon National University)
- 발행 : 2003.10.30
초록
K-means clustering has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research and so on. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using the grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.