  There are partially separable data types that make classification tasks very
hard. In other words, only parts of the data are informative meaning that
looking at the rest of the data would not give any distinguishable hint for
classification. In this situation, the typical assumption of having the whole
labeled data as an informative unit set for classification does not work.
Consequently, typical classification methods with the mentioned assumption fail
in such a situation. In this study, we propose a framework for the
classification of partially separable data types that are not classifiable
using typical methods. An algorithm based on the framework is proposed that
tries to detect separable subgroups of the data using an iterative clustering
approach. Then the detected subgroups are used in the classification process.
The proposed approach was tested on a real dataset for autism screening and
showed its capability by distinguishing children with autism from normal ones,
while the other methods failed to do so.
