DocumentCode :
685817
Title :
A novel parallel implementation of Naive Bayesian classifier for Big Data
Author :
Katkar, Vijay D. ; Kulkarni, S.V.
Author_Institution :
Dept. of Inf. Technol., PimpriChinchwad Coll. of Eng., Pune, India
fYear :
2013
fDate :
12-14 Dec. 2013
Firstpage :
847
Lastpage :
852
Abstract :
Big Data has become one of the most commonly used terms in the Information Technology circles. The sheer volume of data to be processed and analyzed has grown exponentially with the increasing popularity of Internet and World Wide Web. This presents challenges while storing, manipulating and mining the data. Every day researchers are working on different solutions to handle the volume of data being provided. In machine learning, classification of new observations is done on the basis of the provided learning(training) data to the classifiers. One of the most commonly used efficient and accurate classifiers is the Naive Bayesian classifier. This paper proposes a novel parallel implementation of Naive Bayesian (PNB+) classifier to decrease the testing time complexity while handling large data sets.
Keywords :
Bayes methods; Big Data; Internet; Web sites; computational complexity; data mining; learning (artificial intelligence); parallel processing; pattern classification; Big Data; Information Technology; Internet; Naive Bayesian classifier; World Wide Web; data analysis; data manipulation; data mining; data processing; data storage; large data set handling; learning data; machine learning; parallel implementation; time complexity; training data; Algorithm design and analysis; Bayes methods; Instruction sets; Parallel processing; Testing; Time complexity; Training; Big Data; Naive Bayesian; Parallel Computing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Green Computing, Communication and Conservation of Energy (ICGCE), 2013 International Conference on
Conference_Location :
Chennai
Type :
conf
DOI :
10.1109/ICGCE.2013.6823552
Filename :
6823552
Link To Document :
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