Big data clustering with varied density based on MapReduce

Big data clustering with varied density based on MapReduce

Abstract The DBSCAN algorithm is a prevalent method of density-based clustering algorithms, the most important feature of which is the ability to detect arbitrary shapes and varied clusters and noise data. Nevertheless, this algorithm faces a number of challenges, including failure to find clusters...

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Bibliographic Details
Main Authors: Safanaz Heidari, Mahmood Alborzi, Reza Radfar, Mohammad Ali Afsharkazemi, Ali Rajabzadeh Ghatari
Format: Article
Language:English
Published: SpringerOpen 2019-08-01
Series:Journal of Big Data
Subjects:
Map-Reduce
Density-based clustering
Big data
Online Access:http://link.springer.com/article/10.1186/s40537-019-0236-x

Internet

http://link.springer.com/article/10.1186/s40537-019-0236-x

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