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Cluster Analysis by Binary Morphology
February 1993 (vol. 15 no. 2)
pp. 170-180

An approach to unsupervised pattern classification that is based on the use of mathematical morphology operations is developed. The way a set of multidimensional observations can be represented as a mathematical discrete binary set is shown. Clusters are then detected as well separated subsets by means of binary morphological transformations.

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Index Terms:
binary morphology; unsupervised pattern classification; mathematical morphology operations; multidimensional observations; mathematical discrete binary set; well separated subsets; pattern recognition; set theory
Citation:
J.G. Postaire, R.D. Zhang, C. Lecocq-Botte, "Cluster Analysis by Binary Morphology," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 2, pp. 170-180, Feb. 1993, doi:10.1109/34.192490
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