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
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
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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.