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On the Effectiveness of Hybrid Canopy With Hoeffding Adaptive Naive Bayes Trees: Distributed Data Mining for Big Data Analytics

On the Effectiveness of Hybrid Canopy With Hoeffding Adaptive Naive Bayes Trees: Distributed Data Mining for Big Data Analytics

Mrutyunjaya Panda
Copyright: © 2019 |Pages: 15
ISBN13: 9781522575016|ISBN10: 1522575014|EISBN13: 9781522575023
DOI: 10.4018/978-1-5225-7501-6.ch043
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MLA

Panda, Mrutyunjaya. "On the Effectiveness of Hybrid Canopy With Hoeffding Adaptive Naive Bayes Trees: Distributed Data Mining for Big Data Analytics." Web Services: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 788-802. https://doi.org/10.4018/978-1-5225-7501-6.ch043

APA

Panda, M. (2019). On the Effectiveness of Hybrid Canopy With Hoeffding Adaptive Naive Bayes Trees: Distributed Data Mining for Big Data Analytics. In I. Management Association (Ed.), Web Services: Concepts, Methodologies, Tools, and Applications (pp. 788-802). IGI Global. https://doi.org/10.4018/978-1-5225-7501-6.ch043

Chicago

Panda, Mrutyunjaya. "On the Effectiveness of Hybrid Canopy With Hoeffding Adaptive Naive Bayes Trees: Distributed Data Mining for Big Data Analytics." In Web Services: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 788-802. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7501-6.ch043

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Abstract

The Big Data, due to its complicated and diverse nature, poses a lot of challenges for extracting meaningful observations. This sought smart and efficient algorithms that can deal with computational complexity along with memory constraints out of their iterative behavior. This issue may be solved by using parallel computing techniques, where a single machine or a multiple machine can perform the work simultaneously, dividing the problem into sub problems and assigning some private memory to each sub problems. Clustering analysis are found to be useful in handling such a huge data in the recent past. Even though, there are many investigations in Big data analysis are on, still, to solve this issue, Canopy and K-Means++ clustering are used for processing the large-scale data in shorter amount of time with no memory constraints. In order to find the suitability of the approach, several data sets are considered ranging from small to very large ones having diverse filed of applications. The experimental results opine that the proposed approach is fast and accurate.

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