Multidimensional similarity structure analysis
Multidimensional similarity structure analysis
Algorithms for clustering data
Algorithms for clustering data
Visualizing Data
Visual cluster validity for prototype generator clustering models
Pattern Recognition Letters
Scalable visual assessment of cluster tendency for large data sets
Pattern Recognition
A fuzzy-soft competitive learning algorithm for ophthalmological MRI segmentation
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Application of fuzzy subtractive clustering for enzymes classification
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
A fuzzy statistics based method for mining fuzzy correlation rules
WSEAS Transactions on Mathematics
bigVAT: Visual assessment of cluster tendency for large data sets
Pattern Recognition
Will the real iris data please stand up?
IEEE Transactions on Fuzzy Systems
Visual Assessment of Clustering Tendency for Rectangular Dissimilarity Matrices
IEEE Transactions on Fuzzy Systems
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The visual assessment of tendency (VAT) technique, developed by J.C. Bezdek, R.J. Hathaway and J.M. Huband, uses a visual approach to find the number of clusters in data. In this paper, we develop a new algorithm that processes the numeric output of VAT programs, other than gray level images as in VAT, and produces the tendency curves. Possible cluster borders will be seen as high-low patterns on the curves, which can be caught not only by human eyes but also by the computer. Our numerical results are very promising. The program caught cluster structures even in cases where the visual outputs of VAT are virtually useless.