Symbolic clustering using a new dissimilarity measure
Pattern Recognition
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
Adjusting the clustering results referencing an external set
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Attribute value weighting in k-modes clustering
Expert Systems with Applications: An International Journal
Context Oriented Analysis of Interest Reflection of Tweeted Webpages based on Browsing Behavior
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Central clustering of categorical data with automated feature weighting
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this paper, the conventional k-modes-type algorithms for clustering categorical data are extended by representing the clusters of categorical data with k-populations instead of the hard-type centroids used in the conventional algorithms. Use of a population-based centroid representation makes it possible to preserve the uncertainty inherent in data sets as long as possible before actual decisions are made. The k-populations algorithm was found to give markedly better clustering results through various experiments.