Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering
Pattern Recognition Letters
Self-organizing maps
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
Extending the Kohonen self-organizing map networks for clustering analysis
Computational Statistics & Data Analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Clustering Irregular Shapes Using High-Order Neurons
Neural Computation
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Exploring ant-based algorithms for gene expression data analysis
Artificial Intelligence in Medicine
McSOM: Minimal Coloring of Self-Organizing Map
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
A case study on financial ratios via cross-graph quasi-bicliques
Information Sciences: an International Journal
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Classical clustering methods, such as partitioning and hierarchical clustering algorithms, often fail to deliver satisfactory results, given clusters of arbitrary shapes. Motivated by a clustering validity index based on inter-cluster and intra-cluster density, we propose that the clustering validity index be used not only globally to find optimal partitions of input data, but also locally to determine which two neighboring clusters are to be merged in a hierarchical clustering of Self-Organizing Map (SOM). A new two-level SOM-based clustering algorithm using the clustering validity index is also proposed. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data in a better way than classical clustering algorithms on an SOM.