A Rough Set Theory Approach for Rule Generation and Validation Using RSES

Abstract

Despite significant progress in e-learning technology over previous years, in view of huge sizes of data and databases, efficient knowledge extraction techniques are still required to make e-learning effective tool for delivery of learning. Rough set theory approach provides an effective technique for extraction of knowledge out of massive data. In order to provide effective support to learners, it is essential to know individual style of learning for each learner. For determining learning style of each learner, one is required to extract essentials of style of learning from a large number of parameters including academic background, profession, time available etc. In such scenario, rough theory proves a useful tool. In this paper, a rough set theory approach is proposed for determining learning styles of learners efficiently, so that based on the style, a learner may be provided learning support on the basis of requirement of the learner. These is achieved by eliminating redundant and ambiguous data and by generating reduct set, core set and rules from the given data. The results of this study are validated through RSES software by using same rough set analysis.

Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

“…where v k defines a class of element v correspondence on information k. Equation ( 1) depicts an inferior estimate of v with a deep respect to k is the arrangement of all substances, which certainly fits into the vset on the S space based on knowledge k. Equation (2) shows a superior estimation of set v with adoration to k is the arrangement of all things considered, which might probably have a position with set v on the S space based on data k [15].…”

Modeling Rumors in Twitter

“…The study, which extracts meaningful content from large datasets, is called data mining. Data mining has its roots penetrated to different segregated sectors like data mining is used with Rough Set Theory to extract meaningful knowledge from large databases (Rana & Lal, 2016), data mining is used to mine medical data (Banu et al, 2015;Dey et al, 2014), it can be used with steganography techniques (Bhattacharya et al, 2012) and it is also used in systems proposing frameworks for firms dealing customer management relationship (Ranjan & Bhatnagar, 2009).…”

Concept Lattice: A rough set approach

“…FCA and rough set theory are two kinds of complementary mathematical tools for data analysis and data processing ( [26], [27]). Up to now, many efforts have been made to combine these two theories ( [26], [27], [3], [18], [22], [8], [24], [4]), in which the concept lattices based on rough set theory, including the attribute oriented concept lattice [2], lattice for covering rough approximation [14], rough set approach on lattice [12] and distributive lattice [16], rough modular lattice [13], lattice for rough intervals [11] and the object oriented concept lattice ( [26], [27]), are perspective concept lattices for knowledge representation and knowledge discovery. However, the concept lattices usually contain redundant attributes and objects.…”