Identification of Interaction Patterns and Classification with Applications to Microarray Data

Emerging patterns represent a class of interaction structures which
has been recently proposed as a tool in data mining. In this paper,
a new and more general definition refering to underlying
probabilities is proposed. The defined interaction patterns carry
information about the relevance of combinations of variables for
distinguishing between classes. Since they are formally quite
similar to the leaves of a classification tree, we propose a fast
and simple method which is based on the CART algorithm to find the
corresponding empirical patterns in data sets. In simulations, it
can be shown that the method is quite effective in identifying
patterns. In addition, the detected patterns can be used to define
new variables for classification. Thus, we propose a simple scheme
to use the patterns to improve the performance of classification
procedures. The method may also be seen as a scheme to improve the
performance of CARTs concerning the identification of interaction
patterns as well as the accuracy of prediction.