Abstract
Using pattern mining techniques for building a predictive model is currently a popular topic of research. The aim of these techniques is to obtain classifiers of better predictive performance as compared to greedily constructed models, as well as to allow the construction of predictive models for data not represented in attribute-value vectors. In this chapter we provide an overview of recent techniques we developed for integrating pattern mining and classification tasks. The range of techniques spans the entire range from approaches that select relevant patterns from a previously mined set for propositionalization of the data, over inducing patternbased rule sets, to algorithms that integrate pattern mining and model construction. We provide an overview of the algorithms which are most closely related to our approaches in order to put our techniques in a context.
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Bringmann, B., Nijssen, S., Zimmermann, A. (2010). From Local Patterns to Classification Models. In: Džeroski, S., Goethals, B., Panov, P. (eds) Inductive Databases and Constraint-Based Data Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7738-0_6
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