On the stationary state of Kohonen's self-organizing sensory mapping
Biological Cybernetics
Symbolic clustering using a new dissimilarity measure
Pattern Recognition
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence and ordering of Kohonen's batch map
Neural Computation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Suppressed fuzzy c-means clustering algorithm
Pattern Recognition Letters
Clustering and its validation in a symbolic framework
Pattern Recognition Letters
Clustering of interval data based on city-block distances
Pattern Recognition Letters
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
Pattern Recognition Letters
Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation
Pattern Recognition Letters
Fast algorithm and implementation of dissimilarity self-organizing maps
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Dynamic clustering for interval data based on L2 distance
Computational Statistics
New clustering methods for interval data
Computational Statistics
Fuzzy c-means clustering methods for symbolic interval data
Pattern Recognition Letters
Dynamic clustering of interval data using a Wasserstein-based distance
Pattern Recognition Letters
Multilayer SOM with tree-structured data for efficient document retrieval and plagiarism detection
IEEE Transactions on Neural Networks
Unsupervised pattern recognition models for mixed feature-type symbolic data
Pattern Recognition Letters
Dynamic clustering of interval-valued data based on adaptive quadratic distances
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Computing with words with the ontological self-organizing map
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Far beyond the classical data models: symbolic data analysis
Statistical Analysis and Data Mining
Brief overview of symbolic data and analytic issues
Statistical Analysis and Data Mining
Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy clustering for symbolic data
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data
Expert Systems with Applications: An International Journal
Self-Organizing Maps for imprecise data
Fuzzy Sets and Systems
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Kohonen's self-organizing map (SOM) is a competitive learning neural network that uses a neighborhood lateral interaction function to discover the topological structure hidden in the data set. It is an unsupervised learning which has both visualization and clustering properties. In general, the SOM neural network is constructed as a learning algorithm for numeric (vector) data. Although there are different SOM clustering methods for numeric data with real applications in the literature, there is less consideration in a SOM clustering for symbolic data. In this paper, we modify the SOM so that it can treat symbolic data and a so-called symbolic SOM (S-SOM) is then proposed. We first use novel structures to represent symbolic neurons. We then use a suppression concept to create a learning rule for neurons. Therefore, the S-SOM is created for treating symbolic data by embedding the novel structure and the suppression learning rule. Some real data sets are applied with the S-SOM. The experimental results show the feasibility and effectiveness of the proposed S-SOM in these real applications.