artlib.common.VAT

VAT.

cluster tendency. .. # Proceedings of the 2002 International Joint Conference on Neural Networks. .. # doi:10.1109/IJCNN.2002.1007487

Functions

VAT() → Tuple[numpy.ndarray, numpy.ndarray])

Visual Assessment of Cluster Tendency (VAT) algorithm.

Module Contents

artlib.common.VAT.VAT(data: numpy.ndarray, distance_metric: Callable | None = lambda X: ...) → 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.

Parameters:

  • data (np.ndarray) – Input dataset as a 2D numpy array where each row is a sample.

  • distance_metric (callable, optional) – Callable function to calculate pairwise distances. Defaults to Euclidean distance using pdist. If None, assumes data is a pre-computed distance matrix.

Returns:

  • Reordered distance matrix reflecting cluster structure.

  • Reordered list of indices indicating the optimal clustering order.

Return type:

Tuple[np.ndarray, np.ndarray]