Title :
Rough Set Methods for Large and Spare Data in EAV Format
Author :
Wojciech Swieboda;Hung Son Nguyen
Author_Institution :
Inst. of Math., Univ. of Warsaw, Warsaw, Poland
Abstract :
In this article we discuss a computationally effective method for computing approximate decision reducts of large data sets. We consider the EAV (entity-attribute-value) which efficiently stores sparse data sets and we propose new implementations of Maximum Discernibility heuristic for data sets represented in this format.
Keywords :
"Data mining","Information systems","Approximation methods","Set theory","Databases","Partitioning algorithms","Data models"
Conference_Titel :
Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2012 IEEE RIVF International Conference on
Print_ISBN :
978-1-4673-0307-1
DOI :
10.1109/rivf.2012.6169830
