Abstract The advent of Big Data has seen both the sources and volumes of data increase
rapidly. A multi-source information system can be used to represent information drawn from …

Many methods based on the rough set to deal with incomplete information systems have
been proposed in recent years. However, they are only suitable for the incomplete systems …

Density based clustering techniques like DBSCAN are attractive because it can find arbitrary
shaped clusters along with noisy outliers. Its time requirement is O (n2) where n is the size of …

Dynamic updating of attribute reduction is a key factor for the success of rough set theory
since many real data vary dynamically with time. Though many incremental methods for …

Based on the analysis of the rough set model on a tolerance relation and the fuzzy rough
set, two types of fuzzy rough sets models on tolerance relations are constructed and …

Rough set theory is advocated as a framework for conceptualizing and analyzing various
types of data, which is a powerful tool for discovering patterns in a given data set through a …

With granular computing point of view, the classical dominance-based rough set model is
based on a single granulation. For an ordered information system, this paper proposes two …

Incremental learning is an efficient technique for knowledge discovery in a dynamic
database, which enables acquiring additional knowledge from new data without forgetting …

Based on analysis of Pawlak's rough set model in the view of single equivalence relation
and the theory of fuzzy set, associated with multi-granulation rough set models proposed by …

This paper presents rough netrosophic multiattribute decision making based on grey
relational analysis. While the concept of neutrosophic sets is a powerful logic to deal with …