Variable precision rough set model
Journal of Computer and System Sciences
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
On Application of Rough Data Mining Methods to Automatic Construction of Student Models
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
Variable precision rough set for group decision-making: An application
International Journal of Approximate Reasoning
On acquiring classification knowledge from noisy data based on rough set
Expert Systems with Applications: An International Journal
The investigation of the Bayesian rough set model
International Journal of Approximate Reasoning
A novel soft set approach in selecting clustering attribute
Knowledge-Based Systems
The Position of Rough Set in Soft Set: A Topological Approach
International Journal of Applied Metaheuristic Computing
On Quasi Discrete Topological Spaces in Information Systems
International Journal of Artificial Life Research
International Journal of Approximate Reasoning
Review: Educational data mining: A survey and a data mining-based analysis of recent works
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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Computational models of the artificial intelligence such as rough set theory have several applications. Data clustering under rough set theory can be considered as a technique for medical decision making. One possible application is the clustering of student suffering study's anxiety. In this paper, we present the applicability of variable precision rough set model for clustering student suffering studies anxiety. The proposed technique is based on the mean of accuracy of approximation using variable precision of attributes. The datasets are taken from a survey aimed to identify of studies anxiety sources among students at Universiti Malaysia Pahang (UMP). At this stage of the research, we show how variable precision rough set model can be used to groups student in each study's anxiety. The results may potentially contribute to give a recommendation how to design intervention, to conduct a treatment in order to reduce anxiety and further to improve student's academic performance.