Rough sets and Boolean reasoning

By Zdzislaw Pawlak, Andrzej Skowron. Information Sciences, 2007

1,009 citations recorded by the source corpus.

In recent years we witness a rapid growth of interest in rough set theory and its applications, worldwide. The theory has been followed by the development of several software systems that implement rough set operations, in particular for solving knowledge discovery and data mining tasks. Rough sets are applied in domains, such as, for instance, medicine, finance, telecommunication, vibration analysis, conflict resolution, intelligent agents, pattern recognition, control theory, signal analysis, process industry, marketing, etc.We introduce basic notions and discuss methodologies for analyzing data and surveys some applications. In particular we present applications of rough set methods for feature selection, feature extraction, discovery of patterns and their applications for decomposition of large data tables as well as the relationship of rough sets with association rules. Boolean reasoning is crucial for all the discussed methods.We also present an overview of some extensions of the classical rough set approach. Among them is rough mereology developed as a tool for synthesis of objects satisfying a given specification in a satisfactory degree. Applications of rough mereology in such areas like granular computing, spatial reasoning and data mining in distributed environment are outlined.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e4540dbf519e90d7686af3575b782b6e67a609e7

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Citations in the corpus

Rudiments of rough sets (2007, neutral): “, [2,8,12,27,36, 42,48,59,61,65,67,76–82,85,90–92,107,108,221,120,122,123,127,132,133,135,148,149,166,190–195,211,231, 241,244,245,250,265,269,270,275,276,281,282,316,321–323,325,338,351,357]); • bioinformatics (see, e.”
Soft sets and soft rough sets (2011, methodology): “In order to describe and extract the useful information hidden in uncertain data, researchers in mathematics, computer science and related areas have proposed a number of theories such as probability theory, fuzzy set theory [35], intuitionistic fuzzy set theory [2,6], rough set theory [21–25], vagu”
Positive approximation: An accelerator for attribute reduction in rough set theory (2010, background): “The equivalence relation R partitions the set U into disjoint subsets.”
Granular Computing: Perspectives and Challenges (2013, background): “Rough sets [51]–[54], [56] emphasize a roughness of description of a given concept X when being realized in terms of the indiscernibility relation provided in advance.”
Incomplete Multigranulation Rough Set (2010, methodology): “In the past ten years, under different conditions, several extensions of the rough-set model have been accomplished [26]–[28], which include variable precision rough-set model [60], rough-set model based on tolerance relation [13], [14], [32], [45], [47], Bayesian roughset model [46], fuzzy rough-se”