Multisolutional clustering and quantization algorithm (MCQ).
We have developed a novel clustering and quantization algorithm that allows the user to create multiple one-to-one correspondences between the actual data and its transformed (clustered and quantized) values, based on the user's hypothesis regarding the nature of the classification task. The types of problems for which the algorithm can be beneficial are discussed. We report experiments employing simulated and real data that suggest the proposed algorithm may be useful in neural network analysis of various phenomena in medicine and biology.
Department of Transplantation, University of Pittsburgh, Pennsylvania, USA.
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Dvorchik
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Marsh
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Gurari
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Subotin
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Doyle
D000465:Algorithms; D000818:Animals; D001283:Atrioventricular Node / Q000502:physiology*; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004558:Electric Stimulation; D066298:In Vitro Techniques; D008955:Models, Cardiovascular; D009431:Neural Conduction / Q000502:physiology; D016571:Neural Networks, Computer; D011817:Rabbits