ANALISIS CLUSTER UNTUK DATA CAMPURAN KATEGORIK DAN NUMERIK (Cluster Analysis for Mixed Cagtegorical and Numeric Data Types)
Yuliani Setia Dewi
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Various clustering algorithms have been developed to group data into clusters. This paper describes clustering objects with mixed categorical and numeric data types. The methods used are two step clustering and transforms mixed data using nonlinear principal component analysis then groups the output resulted using hierarchical aglomerative clustering. The results show that the number of optimal cluster using both methods have the same optimal number of cluster but the rank of ratios of distance measure and distribution of cluster membership are different.