TY  - JOUR
T1  - A Novel Reduct Algorithm for Dimensionality Reduction with Missing Values Based on Rough Set Theory
AU - , K. Thangavel AU - , A. Pethalakshmi AU - , P. Jaganathan 
JO  - International Journal of Soft Computing
VL  - 1
IS  - 2
SP  - 111
EP  - 117
PY  - 2006
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2006.111.117
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2006.111.117
KW  - Data mining
KW  -rough set theory
KW  -reduct
KW  -missing attribute values
KW  -indiscernibility relation
AB  - 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.
ER  -