Pattern-Based Classification: A Unifying Perspective
  The use of patterns in predictive models is a topic that has received a lot
of attention in recent years. Pattern mining can help to obtain models for
structured domains, such as graphs and sequences, and has been proposed as a
means to obtain more accurate and more interpretable models. Despite the large
amount of publications devoted to this topic, we believe however that an
overview of what has been accomplished in this area is missing. This paper
presents our perspective on this evolving area. We identify the principles of
pattern mining that are important when mining patterns for models and provide
an overview of pattern-based classification methods. We categorize these
methods along the following dimensions: (1) whether they post-process a
pre-computed set of patterns or iteratively execute pattern mining algorithms;
(2) whether they select patterns model-independently or whether the pattern
selection is guided by a model. We summarize the results that have been
obtained for each of these methods.
