Symbolic clustering using a new dissimilarity measure
Pattern Recognition
Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Clustering and its validation in a symbolic framework
Pattern Recognition Letters
Clustering of interval data based on city-block distances
Pattern Recognition Letters
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
Pattern Recognition Letters
Dynamic clustering for interval data based on L2 distance
Computational Statistics
New clustering methods for interval data
Computational Statistics
Symbolic Data Analysis: Conceptual Statistics and Data Mining (Wiley Series in Computational Statistics)
Fuzzy c-means clustering methods for symbolic interval data
Pattern Recognition Letters
Symbolic Data Analysis and the SODAS Software
Symbolic Data Analysis and the SODAS Software
Cluster Analysis
Clustering of symbolic objects using gravitational approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New similarity measures between interval-valued fuzzy sets
Proceedings of the 15th WSEAS international conference on Systems
Self-organizing map for symbolic data
Fuzzy Sets and Systems
Fuzzy Kohonen clustering networks for interval data
Neurocomputing
Clustering interval data through kernel-induced feature space
Journal of Intelligent Information Systems
Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices
Fuzzy Sets and Systems
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data
Expert Systems with Applications: An International Journal
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This paper presents partitioning fuzzy K-means clustering models for interval-valued data based on suitable adaptive quadratic distances. These models furnish a fuzzy partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fit between the fuzzy clusters and their representatives. These adaptive quadratic distances change at each algorithm iteration and can be either the same for all clusters or different from one cluster to another. Moreover, additional interpretation tools for individual fuzzy clusters of interval-valued data, suitable to these fuzzy clustering models, are also presented. Experiments with some interval-valued data sets demonstrate the usefulness of these fuzzy clustering models and the merit of the individual fuzzy cluster interpretation tools.