| Abstract | A brief survey of some unsupervised learning and clustering algorithms is performed based on a classical pattern recognition book. Other unsupervised approaches are also briefly introduced in order to broaden the content of the survey of such a large body of possible approaches. An example from paleontology is used to motivate the unsupervised learning problem, while examples from proteomics, geophysical prospecting and digital remote sensing are very briey mentioned. In addition, an unsupervised learning procedure (theIsodata clustering algorithm) was implemented and results reported. |
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