Clustering of interval data based on city-block distances
Pattern Recognition Letters
Fuzzy c-means clustering methods for symbolic interval data
Pattern Recognition Letters
Text processing simplified ARTMAP neural network
SEPADS'05 Proceedings of the 4th WSEAS International Conference on Software Engineering, Parallel & Distributed Systems
Clustering constrained symbolic data
Pattern Recognition Letters
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
Interval competitive agglomeration clustering algorithm
Expert Systems with Applications: An International Journal
Molecular dynamics-like data clustering approach
Pattern Recognition
Fuzzy Kohonen clustering networks for interval data
Neurocomputing
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Most of the techniques used in the literature in clustering symbolic data are based on the hierarchical methodology, which uses the concept of agglomeration or division as the core of the algorithm. The main contribution of this paper is to formulate a clustering algorithm for symbolic objects based on the gravitational approach. The proposed procedure is based on the physical phenomenon in which a system of particles in space converge to the centroid of the system due to gravitational attraction between the particles. Some pairs of samples called mutual pairs, which have a tendency to gravitate toward each other, are discerned at each stage of this multistage scheme. The notions of cluster coglomerate strength and global coglomerate strength are used for accomplishing or abandoning the process of merging a mutual pair. The methodology forms composite symbolic objects whenever two symbolic objects are merged. The process of merging at each stage, reduces the number of samples that are available for consideration. The procedure terminates at some stage where there are no more mutual pairs available for merging. The efficacy of the proposed methodology is examined by applying it on numeric data and also on data sets drawn from the domain of fat oil, microcomputers, microprocessors, and botany. A detailed comparative study is carried out with other methods and the results are presented