Scalable Knowledge Discovery in Complex Data with Pattern Structures

  • Sergei O. Kuznetsov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

Pattern structures propose a direct way to knowledge discovery in data with structure, such as logical formulas, graphs, strings, tuples of numerical intervals, etc., by defining closed descriptions and discovery tools build upon them: automatic construction of taxonomies, association rules and classifiers. A combination of lazy evaluation with projections of initial data, randomization and parallelization suggest efficient approach which is scalable to big data.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sergei O. Kuznetsov
    • 1
  1. 1.School of Applied Mathematics and Information ScienceNational Research University Higher School of EconomicsMoscowRussia

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