Clustering Categorical Data Using Silhouette Coefficient as a Relocating Measure
Clustering Categorical Data Using Silhouette Coefficient as a Relocating Measure
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DOI：
10.1109/iccima.2007.127
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发表时间：
2007-12
期刊：
影响因子：
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通讯作者：
S. Aranganayagi;K. Thangavel
中科院分区：
文献类型：
--
作者：
S. Aranganayagi;K. Thangavel
聚类分析是一种无监督学习方法，构成了智能数据分析过程的基石。分类数据聚类是数据挖掘的一个重要研究领域.在本文中，我们提出了一种新的算法聚类分类数据。基于最小相异度值将对象分组到簇中。在合并过程中，使用轮廓系数重新定位对象。实验结果表明该方法是有效的。
Cluster analysis is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. Clustering categorical data is an important research area data mining. In this paper we propose a novel algorithm to cluster categorical data. Based on the minimum dissimilarity value objects are grouped into cluster. In the merging process, the objects are relocated using silhouette coefficient. Experimental results show that the proposed method is efficient.