A survey on parallel clustering algorithms for Big Data

Data clustering is one of the most studied data mining tasks. It aims, through various methods, to discover previously unknown groups within the data sets. In the past years, considerable progress has been made in this field leading to the development of innovative and promising clustering algorithm...

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Bibliographic Details
Published in:The Artificial intelligence review Vol. 54; no. 4; pp. 2411 - 2443
Main Authors: Dafir, Zineb, Lamari, Yasmine, Slaoui, Said Chah
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.04.2021
Springer
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Big Data
Classification
Clustering
Computer Science
Context
Data mining
Digital integrated circuits
Field programmable gate arrays
Information retrieval
Microprocessors
Peer relationships
Peer to peer computing
Peers
Social networks
Surveys
means
Algorithms
FPGA
MPI
Big Data
DBSCAN
Spark
Clustering
Data mining
GPU
Multi-cores CPU
MapReduce
ISSN:0269-2821, 1573-7462
Online Access:Get full text
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Summary:Data clustering is one of the most studied data mining tasks. It aims, through various methods, to discover previously unknown groups within the data sets. In the past years, considerable progress has been made in this field leading to the development of innovative and promising clustering algorithms. These traditional clustering algorithms present some serious issues in connection with the speed-up, the throughput, and the scalability. Thus, they can no longer be directly used in the context of Big Data, where data are mainly characterized by their volume, velocity, and variety. In order to overcome their limitations, the research today is heading to the parallel computing concept by giving rise to the so-called parallel clustering algorithms. This paper presents an overview of the latest parallel clustering algorithms categorized according to the computing platforms used to handle the Big Data, namely, the horizontal and vertical scaling platforms. The former category includes peer-to-peer networks, MapReduce, and Spark platforms, while the latter category includes Multi-core processors, Graphics Processing Unit, and Field Programmable Gate Arrays platforms. In addition, it includes a comparison of the performance of the reviewed algorithms based on some common criteria of clustering validation in the Big Data context. Therefore, it provides the reader with an overall vision of the current parallel clustering techniques.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-020-09918-2