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Fitting Pattern Structures to Knowledge Discovery in Big Data

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Formal Concept Analysis (ICFCA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7880))

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Abstract

Pattern structures, an extension of FCA to data with complex descriptions, propose an alternative to conceptual scaling (binarization) by giving direct way to knowledge discovery in complex data such as logical formulas, graphs, strings, tuples of numerical intervals, etc. Whereas the approach to classification with pattern structures based on preceding generation of classifiers can lead to double exponent complexity, the combination of lazy evaluation with projection approximations of initial data, randomization and parallelization, results in reduction of algorithmic complexity to low degree polynomial, and thus is feasible for big data.

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Author information

Authors and Affiliations

  1. Higher School of Economics, National Research University, Pokrovskii bd. 11, 109028, Moscow, Russia

    Sergei O. Kuznetsov

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  1. Sergei O. Kuznetsov
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Editors and Affiliations

  1. IRISA, 263 avenue du Général Leclerc, 35042, Rennes, France

    Peggy Cellier

  2. Computer Science Department, TU Dresden, Nöthnitzer Str. 46, 01062, Dresden, Germany

    Felix Distel

  3. Department of Mathematics, TU Dresden, Zellescher Weg 12-14, 01062, Dresden, Germany

    Bernhard Ganter

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Kuznetsov, S.O. (2013). Fitting Pattern Structures to Knowledge Discovery in Big Data. In: Cellier, P., Distel, F., Ganter, B. (eds) Formal Concept Analysis. ICFCA 2013. Lecture Notes in Computer Science(), vol 7880. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38317-5_17

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  • DOI: https://doi.org/10.1007/978-3-642-38317-5_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38316-8

  • Online ISBN: 978-3-642-38317-5

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