Journal of Big Data, Volume (8), No (1), Year (2021-5)

Title : ( Big data fuzzy C-means algorithm based on bee colony optimization using an Apache Hbase )

Authors: seyyed mohammad razavi , Mohsen Kahani , Samad Paydar ,

Citation: BibTeX | EndNote

Abstract

Clustering algorithm analysis, including time and space complexity analysis, has always been discussed in the literature. The emergence of big data has also created a lot of challenges for this issue. Because of high complexity and execution time, traditional clustering techniques cannot be used for such an amount of data. This problem has been addressed in this research. To present the clustering algorithm using a bee colony algorithm and high-speed read/write performance, Map-Reduce architecture is used. Using this architecture allows the proposed method to cluster any volume of data, and there is no limit to the amount of data. The presented algorithm has good performance and high precision. The simulation results on 3 datasets show that the presented algorithm is more efcient than other big data clustering methods. Also, the results of our algorithm execution time on huge datasets are much better than other big data clustering approaches.

Keywords

, Clustering, Big data, Bee colony, MapReduce
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@article{paperid:1084831,
author = {Razavi, Seyyed Mohammad and Kahani, Mohsen and Paydar, Samad},
title = {Big data fuzzy C-means algorithm based on bee colony optimization using an Apache Hbase},
journal = {Journal of Big Data},
year = {2021},
volume = {8},
number = {1},
month = {May},
issn = {2196-1115},
keywords = {Clustering; Big data; Bee colony; MapReduce},
}

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%0 Journal Article
%T Big data fuzzy C-means algorithm based on bee colony optimization using an Apache Hbase
%A Razavi, Seyyed Mohammad
%A Kahani, Mohsen
%A Paydar, Samad
%J Journal of Big Data
%@ 2196-1115
%D 2021

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