Real data may be mixed data that is a combination of continuous,
ordinal, nominal variables. When we start clustering, it is necessary
to understand the characteristics of data. In this paper, we conduct
clustering according to the latent variable form of the given data.
To do so, we will estimate optimal scores for ordinal variables by
minimizing the loss function of PCA. Then, with a di_x000B_erence from
the most representative model based clustering methods, Mclust and
ClustMD, we propose a new clustering algorithm to overcome their
disadvantages. Through numerical study, we compare their perfor-
mances in accuracy and computing time when the label is known.
Finally, we apply the new method to a real data, Byar data.
