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Fitting Pattern Structures for Knowledge Discovery in Big Data

P. 254-266.

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.

В книге

Под редакцией: P. Cellier, F. Distel, B. Ganter. Vol. 7880. Springer, 2013.