Is VAT really single linkage in disguise?
Annals of Mathematics and Artificial Intelligence
A stage by stage pruning algorithm for detecting the number of clusters in a dataset
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Finding number of clusters using VAT image, PBM index and genetic algorithms
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
iVAT and aVAT: enhanced visual analysis for cluster tendency assessment
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
An on-line learning method for face association in personal photo collection
Image and Vision Computing
Expert Systems with Applications: An International Journal
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Clustering is a popular tool for exploratory data analysis. One of the major problems in cluster analysis is the determination of the number of clusters in unlabeled data, which is a basic input for most clustering algorithms. In this paper we investigate a new method called DBE (Dark Block Extraction) for automatically estimating the number of clusters in unlabeled data sets, which is based on an existing algorithm for Visual Assessment of cluster Tendency (VAT) of a data set, using several common image and signal processing techniques. Basic steps include: 1) Generating a VAT image of an input dissimilarity matrix; 2) Performing image segmentation on the VAT image to obtain a binary image, followed by directional morphological filtering; 3) Applying a distance transform to the filtered binary image and projecting the pixel values onto the main diagonal axis of the image to form a projection signal; 4) Smoothing the projection signal, computing its first-order derivative, and then detecting major peaks and valleys in the resulting signal to decide the number of clusters. Our new DBE method is nearly "automatic", depending on just one easy-to-set parameter. Several numerical and real-world examples are presented to illustrate the effectiveness of DBE.