The quick dynamic clustering method for mixed-type data

Authors:
V. V. Ayuyev;A. Thura;N. N. Hlaing;M. B. Loginova
Affiliations:
Kaluga Branch of Bauman Moscow State Technical University, Kaluga, Russia;Kaluga Branch of Bauman Moscow State Technical University, Kaluga, Russia;Kaluga Branch of Bauman Moscow State Technical University, Kaluga, Russia;Kaluga Branch of Bauman Moscow State Technical University, Kaluga, Russia
Venue:
Automation and Remote Control
Year:
2012

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Abstract

This paper describes a new approach to high-dimensional mixed-type data clustering with missing values, which combines information on common nearest neighbors with classic between-vectors distances calculated by an original technique. The results are applied to form intersecting clusters for every missing value.