TY - JOUR
T1 - Best K: the Critical Clustering Structures in Categorical Data
Y1 - 2008
A1 - Ling Liu
A1 - Keke Chen
KW - best Ks'
KW - how can we efficiently and reliably determine the best Ks?
KW - none has satisfactorily addressed the problem of Best K for categorical clustering. Since categorical data does not have an inherent distance function as the similarity measure
KW - surprisingly
KW - The demand on cluster analysis for categorical data continues to grow over the last decade. A well-known problem in categorical clustering is to determine the best K number of clusters. Although several categorical clustering algorithms have been develope
KW - traditional cluster validation techniques based on geometric shapes and density distributions are not appropriate for categorical data. In this paper
KW - we study the entropy property between the clustering results of categorical data with different K number of clusters
KW - what is the set of candidate '
AB - The demand on cluster analysis for categorical data continues to grow over the last decade. A well-known problem in categorical clustering is to determine the best K number of clusters. Although several categorical clustering algorithms have been developed, surprisingly, none has satisfactorily addressed the problem of Best K for categorical clustering. Since categorical data does not have an inherent distance function as the similarity measure, traditional cluster validation techniques based on geometric shapes and density distributions are not appropriate for categorical data. In this paper, we study the entropy property between the clustering results of categorical data with different K number of clusters, and propose the BKPlot method to address the three important cluster validation problems: 1) How can we determine whether there is significant clustering structure in a categorical dataset? 2) If there is significant clustering structure, what is the set of candidate 'best Ks'? 3) If the dataset is large, how can we efficiently and reliably determine the best Ks?
ER -