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
A conceptual version of the K-means algorithm
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
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
Symbolic Data Analysis and the SODAS Software
Symbolic Data Analysis and the SODAS Software
Clustering of symbolic objects using gravitational approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-organizing map for symbolic data
Fuzzy Sets and Systems
Vehicle image classification based on edge: features and distances comparison
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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
Robust support vector machine-trained fuzzy system
Neural Networks
Dynamic clustering of histogram data based on adaptive squared Wasserstein distances
Expert Systems with Applications: An International Journal
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Unsupervised pattern recognition methods for mixed feature-type symbolic data based on dynamical clustering methodology with adaptive distances are presented. These distances change at each algorithm's iteration and can either be the same for all clusters or different from one cluster to another. Moreover, the methods need a previous pre-processing step in order to obtain a suitable homogenization of the mixed feature-type symbolic data into histogram-valued symbolic data. The presented dynamic clustering algorithms have then as input a set of vectors of histogram-valued symbolic data and they furnish a partition and a prototype to each cluster by optimizing an adequacy criterion based on suitable adaptive squared Euclidean distances. To show the usefulness of these methods, examples with synthetic symbolic data sets as well as applications with real symbolic data sets are considered. Moreover, various tools suitable for interpreting the partition and the clusters given by these algorithms are also presented.