A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles

@article{Rana2017ACS,
  title={A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles},
  author={Hemant Rana and Manohar Lal},
  journal={IJDSST},
  year={2017},
  volume={9},
  pages={1-20}
}
Handling of missing attribute values are a big challenge for data analysis. For handling this type of problems, there are some well known approaches, including Rough Set Theory (RST) and classification via clustering. In the work reported here, RSES (Rough Set Exploration System) one of the tools based on RST approach, and WEKA (Waikato Environment for… CONTINUE READING