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http://dx.doi.org/10.5351/KJAS.2004.17.3.535

Tree Based Cluster Analysis Using Reference Data  

최대우 (한국외국어대학교 정보통계학과)
구자용 (고려대학교 통계학과)
최용석 (한국외국어대학교 수학과 통계전공)
Publication Information
The Korean Journal of Applied Statistics / v.17, no.3, 2004 , pp. 535-545 More about this Journal
Abstract
The clustering method suggested in this paper produces clusters based on the 'rules of variables' by merging the 'training' and the identically structured reference data and then by filtering it to obtain the clusters of the 'training data' through the use of the 'tree classification model'. The reference dataset is generated by spatially contrasting it to the 'training data' through the 'reverse arcing' algorithm to effectively identify the clusters. The strength of this method is that it can be applied even to the mixture of continuous and discrete types of 'training data' and the performance of this algorithm is illustrated by applying it to the simulated data as well as to the actual data.
Keywords
reverse-arcing Cluster analysis; Tree model; Reference data; Reverse-arcing;
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