Title :
A Parallel Method for Rough Entropy Computation Using MapReduce
Author :
Si-Yuan Jing ; Jin Yang ; Kun She
Author_Institution :
Sch. of Comput. Sci., Leshan Normal Univ., Leshan, China
Abstract :
Rough set theory has been proven to be a successful computational intelligence tool. Rough entropy is a basic concept in rough set theory and it is usually used to measure the roughness of information set. Existing algorithms can only deal with small data set. Therefore, this paper proposes a method for parallel computation of entropy using MapReduce, which is hot in big data mining. Moreover, corresponding algorithm is also put forward to handle big data set. Experimental results show that the proposed parallel method is effective.
Keywords :
Big Data; data mining; entropy; mathematics computing; parallel programming; rough set theory; MapReduce; big data mining; big data set handling; computational intelligence tool; information set roughness measurement; parallel computation method; rough entropy computation; rough set theory; Big data; Clustering algorithms; Computers; Data mining; Entropy; Information entropy; Set theory; Data Mining; Entropy; Hadoop; MapReduce; Rough set theory;
Conference_Titel :
Computational Intelligence and Security (CIS), 2014 Tenth International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4799-7433-7
DOI :
10.1109/CIS.2014.41
