Issue No. 02 - February (1993 vol. 15)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.192490
<p>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.</p>
binary morphology; unsupervised pattern classification; mathematical morphology operations; multidimensional observations; mathematical discrete binary set; well separated subsets; pattern recognition; set theory
C. Lecocq-Botte, R. Zhang and J. Postaire, "Cluster Analysis by Binary Morphology," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 170-180, 1993.