17 April 2006 Performance evaluation based on cluster validity indices in medical imaging
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Exploratory data-driven methods such as unsupervised clustering are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). The major problem with clustering of real bioimaging data is that of deciding how many clusters are present. This motivates the application of cluster validity techniques in order to quantitatively evaluate the results of the clustering algorithm. In this paper, we apply three different cluster validity techniques, namely, Kim's index, Calinski Harabasz index, and the intraclass index to the evaluation of the clustering results of fMRI data. The benefits and major limitations of these cluster validity techniques are discussed based on the achieved results of several datasets.
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Oliver Lange, Oliver Lange, Anke Meyer-Bäse, Anke Meyer-Bäse, Axel Wismüller, Axel Wismüller, } "Performance evaluation based on cluster validity indices in medical imaging", Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 624714 (17 April 2006); doi: 10.1117/12.660670; https://doi.org/10.1117/12.660670

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