@article{MAKHILLIJSC20061220751,
    title = {A Novel Reduct Algorithm for Dimensionality Reduction with Missing Values Based on Rough Set Theory},
    journal = {International Journal of Soft Computing},
    volume = {1},
    number = {2},
    pages = {111-117},
    year = {2006},
    issn = {1816-9503},
    doi = {ijscomp.2006.111.117},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2006.111.117},
    author = {K. Thangavel,A. Pethalakshmi and},
    keywords = {Data mining,rough set theory,reduct,missing attribute values,indiscernibility relation},
    abstract = {Database with missing values is a common phenomenon in data mining, statistical analysis, as well
as in machine learning. Missing values in the database will affect the classification accuracy and effectiveness
of classification rules. In this study, we have used four different methods such as Indiscernibility,
Mean, Median and Mode for dealing with missing attribute values and proposed a Revised Quickreduct
algorithm for dimensionality reduction. A comparative study is also performed with Revised and original
Quickreduct algorithms based on the four different methods. The public domain datasets available in UCI
machine learning repository with missing attribute values are used.}
    }