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• W74494933 startingPage "254" @default.
• W74494933 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." @default.
• W74494933 created "2016-06-24" @default.
• W74494933 creator A5004960011 @default.
• W74494933 date "2013-01-01" @default.
• W74494933 modified "2023-09-29" @default.
• W74494933 title "Fitting Pattern Structures to Knowledge Discovery in Big Data" @default.
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• W74494933 doi "https://doi.org/10.1007/978-3-642-38317-5_17" @default.
• W74494933 hasPublicationYear "2013" @default.
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