Dimension Reduction Using RST and Map Reduce
Karishma Aatar, Shilpa Chougale
Progress in Computing Applications, Vol. 4, No. 1, 2015.
DOI: 
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Abstract:
Nowadays, handling with massive data at big rate or large-scale data mining has become new task. Rough set theory for knowledge learning or developing is applied in data mining. In this project we are going to present a serial as well
as a parallel method [1]. These serial and parallel methods are used for computing rough set approximations. We will
implement the parallel algorithm on Hadoop Map Reduce platform [3, 5]. We will evaluate the performance of three parallel
algorithms with respect to its speedup, scale up and size up[7]. The Hadoop Map Reduce platform is a programming model
and also it is a software framework for developing applications. The Hadoop Map Reduce platform works on massive data
and also it the data gets processed rapidly in parallel on large clusters of compute notes. In this project, we will evaluate only
the efficiency of the algorithms and not its accuracy. By using parallel method on Hadoop platform, due to transparent spilling, the data size limit will be get removed. However, algorithms based on rough set theory are quite a challenging task for the applications of enlarged data.