Comparison of Cluster Analysis Approaches for Binary Data

Cluster methods allow to partition observations into homogeneous groups. Standard cluster analysis approaches consider the variables used to partition observations as continuous. In this work, we deal with the particular case all variables are binary. We focused on two specific methods that can handle binary data: the monothetic analysis and the model-based co-clustering. The aim is to compare the outputs performing these two methods on a common dataset, and figure out how they differ. The dataset on which the two methods are performed is a UNESCO dataset made up of 58 binary variables concerning the ability of UNESCO management to use Internet to promote world heritage sites.