Algorithms for clustering data
Algorithms for clustering data
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
Clustering of Symbolic Data and Its Validation
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
ITERATE: a conceptual clustering algorithm for data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
Projected clustering for categorical datasets
Pattern Recognition Letters
k-ANMI: A mutual information based clustering algorithm for categorical data
Information Fusion
Density of Closed Balls in Real-Valued and Autometrized Boolean Spaces for Clustering Applications
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Clustering constrained symbolic data
Pattern Recognition Letters
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
McSOM: Minimal Coloring of Self-Organizing Map
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Symbolic classification, clustering and fuzzy radial basis function network
Fuzzy Sets and Systems
A new greedy algorithm for improving b-coloring clustering
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
A partially dynamic clustering algorithm for data insertion and removal
DS'07 Proceedings of the 10th international conference on Discovery science
Toward improving re-coloring based clustering with graph b-coloring
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
A graph based framework for clustering and characterization of SOM
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Constraint selection for semi-supervised topological clustering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Self-organizing map for symbolic data
Fuzzy Sets and Systems
Constrained graph b-coloring based clustering approach
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Different Aspects of Clustering The Self-Organizing Maps
Neural Processing Letters
A re-coloring approach for graph b-coloring based clustering
International Journal of Knowledge-based and Intelligent Engineering Systems
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Clustering of symbolic data, using different validity indices, is proposed for determining the optimal number of meaningful clusters. Symbolic objects include linguistic, nominal, boolean, and interval-type of features, along with quantitative attributes. Clustering in this domain involves the use of symbolic dissimilarity between the objects. The novelty of the method lies in transforming the different clustering validity indices, like Normalized Modified Hubert's statistic, Davies-Bouldin index and Dunn's index, from the quantitative domain to the symbolic framework. The effectiveness of symbolic clustering is demonstrated on several real life benchmark data sets.