In real-applications, there may exist many kinds of data (eg, boolean, categorical, real-
valued and set-valued data) and missing data in an information system which is called as a …

Approximations of a concept by a variable precision rough-set model (VPRS) usually vary
under a dynamic information system environment. It is thus effective to carry out incremental …

Rule induction method based on rough set theory (RST) has received much attention
recently since it may generate a minimal set of rules from the decision system for real-life …

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 …

Set-valued information systems are important type of data tables and generalized models of
single-valued information systems. Approximations are the focal point of approaches to …

Data in real-world applications are typically changing with time and are often incomplete. To
address the challenge of processing such dynamic and incomplete data, we propose a …

Abstract The fuzzy Information System over Two Universes (ISTU) formalizing a data table
corresponding to two universes as well as their relations is common in real-world …

Knowledge discovery in databases (KDD) techniques have been extensively employed to
extract knowledge from massive data stores to support decision making in a wide range of …

We present PPQ-trajectory, a spatio-temporal quantization based solution for querying large
dynamic trajectory data. PPQ-trajectory includes a partition-wise predictive quantizer (PPQ) …

Nowadays, data scientists prefer “easy” high-level languages like R and Python, which
accomplish complex mathematical tasks with a few lines of code, but they present memory …