A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Fuzzy clustering of categorical data using fuzzy centroids
Pattern Recognition Letters
On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving k-modes algorithm considering frequencies of attribute values in mode
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
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In [1], recently we proposed a generalization of the frequency-based cluster prototype [2-4], in the same framework of the Fuzzy C-Means clustering algorithm, for the objects of mixed features. In the generalization, a general dissimilarity measure, not the simple matching dissimilarity, is assumed for each categorical feature. In this paper we develop an adaptive method to learn dissimilarity measures for categorical features. We include the method into the framework of the Fuzzy C-Means algorithm so that the clustering algorithm can use the dissimilarity measures rather than the simple matching dissimilarity measure for categorical features. Through the experiments over real object sets, we show the clustering quality becomes better.