We propose a model-based parametric unsupervised sequential classifier
with an information criterion window (ICW), based on the assumption t
hat the number of clusters changes smoothly from a stage to the follow
ing stage, to determine the number of clusters at each stage and to pe
rform classification on the observed mixture data. The proposed approa
ch provides possible solutions for two problems of pattern recognition
: adaptive classification and sequential clustering validation. Simula
tion results demonstrate that the proposed algorithm provides good res
ults for pure mixed data and successfully performs clustering for the
mixed Gaussian data in the large-sample limit.