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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Kuznetsov, S.O. (2013). Scalable Knowledge Discovery in Complex Data with Pattern Structures. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2013. Lecture Notes in Computer Science, vol 8251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45062-4_3
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DOI: https://doi.org/10.1007/978-3-642-45062-4_3
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