  The main contribution of this paper is the development of a new decision tree
algorithm. The proposed approach allows users to guide the algorithm through
the data partitioning process. We believe this feature has many applications
but in this paper we demonstrate how to utilize this algorithm to analyse data
sets containing missing values. We tested our algorithm against simulated data
sets with various missing data structures and a real data set. The results
demonstrate that this new classification procedure efficiently handles missing
values and produces results that are slightly more accurate and more
interpretable than most common procedures without any imputations or
pre-processing.
