한국데이터정보과학회:학술대회논문집
- 2005.04a
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- Pages.185-192
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- 2005
K-means Clustering for Environmental Indicator Survey Data
- Park, Hee-Chang (Department of Statistics, Changwon National University) ;
- Cho, Kwang-Hyun (Department of Statistics, Changwon National University)
- Published : 2005.04.29
Abstract
There are many data mining techniques such as association rule, decision tree, neural network analysis, clustering, genetic algorithm, bayesian network, memory-based reasoning, etc. We analyze 2003 Gyeongnam social indicator survey data using k-means clustering technique for environmental information. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper, we used k-means clustering of several clustering techniques. The k-means clustering is classified as a partitional clustering method. We can apply k-means clustering outputs to environmental preservation and environmental improvement.