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Performance Analysis of aScalable Naïve Bayes Classifier on Beyond MapReduce

http://hdl.handle.net/20.500.12678/0000006255
66a459d9-3d62-43ee-921e-1b210e33253e
b50bccc7-767f-432f-b630-66802572f25c
None
Name / File License Actions
Performance Performance Analysis of aScalable Naïve Bayes Classifier on Beyond MapReduce.pdf (1.6 Mb)
© 2017 ICAIT
Publication type
Conference paper
Upload type
Publication
Title
Title Performance Analysis of aScalable Naïve Bayes Classifier on Beyond MapReduce
Language en
Publication date 2017-11-02
Authors
Myat Cho Mon Oo
Thandar Thein
Description
Many real world areas from different sources generate the massive data with large volume of high velocity, complex and variable data. The massive data becomes a challenge for machine learning algorithm because they are difficult to process and extract knowledge using traditional analysis tools. Therefore, a massively scalable and parallel algorithm is needed to process and analyze such massive data. Recently Hadoop MapReduce framework has been adapted for processing large data in an extremely parallel mining. MapReduce may not a very good fit for most of the scalable machine learning that Mahout pioneered. For large scale machine learning on distributed system, Mahout Samsarais used with efficient distributed execution on Spark. This paper analyses the scalability of Naïve Bayes classifier which is implemented by Mahout Samsara. The performance of scalable Naïve Bayes classifier (SNB) on Beyond MapReduce and traditional Naïve Bayes classifier arealso comparedover different data sets. The experimental results show that SNB on Beyond MapReduceis more suitable to classify massive datasets in distributed computing environment and it provides a better accuracy and minimal processing time than traditional Naïve Bayes classifier.
Conference papers
ICAIT 2017
1-2 November, 2017
1st International Conference on Advanced Information Technologies
Yangon, Myanmar
Cloud Computing and Big Data Analytics
https://www.uit.edu.mm/icait-2017/
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