Guest Editorial: Global modeling using local patterns
Johannes Fürnkranz, Arno Knobbe · Data Mining and Knowledge Discovery · 2010
Overthelastdecade,localpatterndiscoveryhasbecomearapidlygrowingfield(Moriketal.2005),andarangeoftechniquesisavailableforproducingextensivecollectionsofpatterns.Becauseoftheexhaustivenatureofmostsuchtechniques,thepatterncol-lections provide a fairly complete picture of the information content of the database.However,suchso-calledlocalpatternsrepresentfragmentedknowledge,anditisoftennot clear how the pieces of the puzzle can be combined into a global model, which isoften the desirable result of a data mining process. Thus, the question of how to turnlarge collections of patterns into global models deserves attention.This special issue of the Data Mining and Knowledge Discovery Journal featuresa number of papers that represent the state of the art in building global models fromlocal patterns. In our view, a common ground of all the local pattern mining tech-niquesisthattheycanbeconsidered tobefeatureconstructiontechniques thatfollowdifferent objectives (or constraints). We will see that the redundancy of these patternsandtheselectionofsuitablesubsetsofpatternsareaddressedinseparatesteps,sothateach resulting feature is highly informative in the context of the global data miningproblem.In earlier work (Knobbe et al. 2008), a framework was proposed that provides ageneral outline of the activities involved. The framework, called From Local Patterns