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
A Modified Chi2 Algorithm for Discretization
IEEE Transactions on Knowledge and Data Engineering
Degree of Similarity in Fuzzy Partition
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
A unified framework for model-based clustering
The Journal of Machine Learning Research
Khiops: A Statistical Discretization Method of Continuous Attributes
Machine Learning
Fuzzy Conditional Probability Relations and their Applications in Fuzzy Information Systems
Knowledge and Information Systems
Fuzzy clustering of categorical data using fuzzy centroids
Pattern Recognition Letters
An Extended Chi2 Algorithm for Discretization of Real Value Attributes
IEEE Transactions on Knowledge and Data Engineering
On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity relations and fuzzy orderings
Information Sciences: an International Journal
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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Ordinal feature values are totally ordered labels that can be considered as fuzzy sets. The formulation of proper fuzzy sets for ordinal labels is important for the systems that deal with the objects of mixed feature types. When a proper ordinal-numerical mapping for an ordinal feature of interest is given, proper fuzzy sets for the labels of the ordinal feature can be easily formulated. In this paper, we propose an adaptive method to learn proper ordinal-numerical mappings for ordinal features of interest from a given objects of mixed features including the ordinal features. The method starts with uniform ordinal-numerical mappings, and performs two steps iteratively. The first step computes a fuzzy partition over the given object set with the ordinal-numerical mappings. The second step learns new ordinal-numerical mappings from the new fuzzy partition in the way that the new mappings make the similarity between two ordinal labels be similar to the average similarity between the objects having the two labels, respectively. Through the alternate repetition of the two steps, both of the ordinal-numerical mappings and the clustering quality become gradually improved. The validity of the proposed method is strongly supported through the experiments with a modified fuzzy C-means clustering algorithm in which the proposed method is implemented.