artlib.common.VAT
=================

.. py:module:: artlib.common.VAT

.. autoapi-nested-parse::

   VAT.

   .. # Bezdek, J. C., & Hathaway, R. J. (2002). .. # VAT: A tool for visual assessment of
   cluster tendency. .. # Proceedings of the 2002 International Joint Conference on Neural
   Networks. .. # doi:10.1109/IJCNN.2002.1007487



Functions
---------

.. autoapisummary::

   artlib.common.VAT.VAT


Module Contents
---------------

.. py:function:: VAT(data: numpy.ndarray, distance_metric: Optional[Callable] = lambda X: pdist(X, 'euclidean')) -> Tuple[numpy.ndarray, numpy.ndarray]

   Visual Assessment of Cluster Tendency (VAT) algorithm.

   VAT was originally designed as a visualization tool for clustering behavior of data.
   When the VAT-reordered distance matrix is plotted as an image, clusters will appear
   in visually distinct groups along the diagonal. However, it has since been
   discovered that the reordering significantly improves the results of order-dependent
   clustering methods like ART. It is therefore recommended to pre-process data with
   VAT prior to presentation when possible.

   .. # Bezdek, J. C., & Hathaway, R. J. (2002).
   .. # VAT: A tool for visual assessment of cluster tendency.
   .. # Proceedings of the 2002 International Joint Conference on Neural Networks.
   .. # doi:10.1109/IJCNN.2002.1007487

   .. bibliography:: ../../references.bib
      :filter: citation_key == "bezdek2002vat"

   :param data: Input dataset as a 2D numpy array where each row is a sample.
   :type data: np.ndarray
   :param distance_metric: Callable function to calculate pairwise distances. Defaults to Euclidean
                           distance using `pdist`. If None, assumes data is a pre-computed distance matrix.
   :type distance_metric: callable, optional

   :returns:

             - Reordered distance matrix reflecting cluster structure.
             - Reordered list of indices indicating the optimal clustering order.
   :rtype: Tuple[np.ndarray, np.ndarray]