Representatives of Rough Regions for Generating Classification Rules

Part 2: Rough Set Methods for Big Data AnalyticsInternational audienceRough set theory provides a useful tool for describing uncertain concepts. The description of a given concept constructed based on rough regions can be used to improve the quality of classification. Processing large data using rough set methods requires efficient implementations as well as alternative approaches to speed up computations. This paper proposes a representative-based approach for rough region-based classification. Positive, boundary, and negative regions are replaced with their representatives sets that preserve information needed for generating classification rules. For data divisible into a relatively low number of equivalence classes representatives sets are considerably smaller than the whole regions. Using a small representation of regions significantly speeds up the process of rule generation

Granular-relational data mining: how to mine relational data in the paradigm of granular computing ?

This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining relational data. Lastly, the book offers all readers interested in computational intelligence in the broader sense the opportunity to deepen their understanding of the newly emerging field granular-relational data mining