Hierarchical clustering of mixed data based on distance hierarchy
Information Sciences: an International Journal
Incremental clustering of mixed data based on distance hierarchy
Expert Systems with Applications: An International Journal
Clustering Hierarchical Data Using Self-Organizing Map: A Graph-Theoretical Approach
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
A novel audio color watermarking scheme based on self-organizing map
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Gastroenterology dataset clustering using possibilistic Kohonen maps
WSEAS Transactions on Information Science and Applications
Probabilistic self-organizing maps for qualitative data
Neural Networks
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
A computational intelligence scheme for the prediction of the daily peak load
Applied Soft Computing
Modified adaptive resonance theory network for mixed data based on distance hierarchy
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
A dissimilarity measure for the k-Modes clustering algorithm
Knowledge-Based Systems
Growing Self-Organizing Map with cross insert for mixed-type data clustering
Applied Soft Computing
A self-organizing map for transactional data and the related categorical domain
Applied Soft Computing
Incorporating linear discriminant analysis in neural tree for multidimensional splitting
Applied Soft Computing
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The self-organizing map (SOM) is an unsupervised neural network which projects high-dimensional data onto a low-dimensional grid and visually reveals the topological order of the original data. Self-organizing maps have been successfully applied to many fields, including engineering and business domains. However, the conventional SOM training algorithm handles only numeric data. Categorical data are usually converted to a set of binary data before training of an SOM takes place. If a simple transformation scheme is adopted, the similarity information embedded between categorical values may be lost. Consequently, the trained SOM is unable to reflect the correct topological order. This paper proposes a generalized self-organizing map model that offers an intuitive method of specifying the similarity between categorical values via distance hierarchies and, hence, enables the direct process of categorical values during training. In fact, distance hierarchy unifies the distance computation of both numeric and categorical values. The unification is done by mapping the values to distance hierarchies and then measuring the distance in the hierarchies. Experiments on synthetic and real datasets were conducted, and the results demonstrated the effectiveness of the generalized SOM model.