Nikolaos Tsapanos

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Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are(More)
In this paper, a novel algorithm for shape matching based on the Hausdorff distance and a binary search tree data structure is proposed. The shapes are stored in a binary search tree that can be traversed according to a Hausdorff-like similarity measure that allows us to make routing decisions at any given internal node. Each node functions as a classifier(More)
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