Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity
Pattern Recognition Letters
A conceptual version of the K-means algorithm
Pattern Recognition Letters
A monothetic clustering method
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
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)
Symbolic Data Analysis and the SODAS Software
Symbolic Data Analysis and the SODAS Software
Dynamic clustering of interval data using a Wasserstein-based distance
Pattern Recognition Letters
Cluster Analysis
Clustering of symbolic objects using gravitational approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A supervised clustering and classification algorithm for mining data with mixed variables
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Clustering-Based Approach for Integrating Document-Category Hierarchies
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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
Dynamic clustering of histogram data based on adaptive squared Wasserstein distances
Expert Systems with Applications: An International Journal
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This paper presents partitioning dynamic clustering methods for interval-valued data based on suitable adaptive quadratic distances. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These adaptive quadratic distances change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Moreover, various tools for the partition and cluster interpretation of interval-valued data are also presented. Experiments with real and synthetic interval-valued data sets show the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools.