Robust dynamic clustering

M.A.F. Aboukalam and H. AI‐Nachawati

Statistica Neerlandica, 1992, vol. 46, issue 2‐3, 143-152

Abstract: The dynamic clustering (DC) algorithm is a method for discovering clusters in a given population. Unfortunately the classical DC algorithms fail to perform well in the presence of outliers. A robust dynamic clustering (RDC) algorithm is introduced to overcome this problem. Robust estimates of the location vector and the covariance matrix are calculated in the affine invariant case. A simulation study is presented to demonstrate the basic difference between the DC and the RDC algorithms. Three kinds of optimization criteria are used in case of contaminated multivariate normal distributions.

Date: 1992
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