(Automatic) Cluster Count Extraction from Unlabeled Data Sets
Isaac J. Sledge, Jacalyn M. Huband, James C. Bezdek · 2008
Through the years researchers have crafted algorithms to carry out the process of object partitioning (clustering). All clustering algorithms ultimately rely on human inputs, principally in the form of the number of clusters to seek. This work investigates a new technique for automating cluster assessment and estimating the number of clusters to look for in unlabeled data utilizing the VAT [visual assessment of cluster tendency] algorithm coupled with common image processing techniques. Several numerical examples are presented to illustrate the effectiveness of the proposed method.