Scalable Knowledge Discovery in Complex Data with Pattern Structures

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.