An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm

@article{Havens2012AnEF,
  title={An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm},
  author={Timothy C. Havens and James C. Bezdek},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2012},
  volume={24},
  pages={813-822}
}
The VAT algorithm is a visual method for determining the possible number of clusters in, or the cluster tendency of a set of objects. The improved VAT (iVAT) algorithm uses a graph-theoretic distance transform to improve the effectiveness of the VAT algorithm for “tough” cases where VAT fails to accurately show the cluster tendency. In this paper, we present an efficient formulation of the iVAT algorithm which reduces the computational complexity of the iVAT algorithm from O(N3) to O(N2). We… CONTINUE READING
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