A Framework for the Comparison of Complex Patterns

Data mining and knowledge discovery techniques are commonly used to extract condensed artifacts representing huge volumes of data. The comparison of such compact and rich in semantics representations (which we call patterns) can be useful to avoid the direct comparison of underlying raw data. In this paper, we present a general framework for the assessment of similarity between patterns, by identifying the common features that characterize approaches proposed in the literature for particular applications. We also propose an implementation of the framework using an UML formalism, and discuss efficiency issues that arise when similarity queries are considered, i.e. when a similarity predicate is used to query a collection of pattern.