Scalable Clustering Algorithms for Big Data: A Review

Abstract

Clustering algorithms have become one of the most critical research areas in multiple domains, especially data mining. However, with the massive growth of big data applications in the cloud world, these applications face many challenges and difficulties. Since Big Data refers to an enormous amount of data, most traditional clustering algorithms come with high computational costs. Hence, the research question is how to handle this volume of data and get accurate results at a critical time. Despite ongoing research work to develop different algorithms to facilitate complex clustering processes, there are still many difficulties that arise while dealing with a large volume of data. In this paper, we review the most relevant clustering algorithms in a categorized manner, provide a comparison of clustering methods for large-scale data and explain the overall challenges based on clustering type. The key idea of the paper is to highlight the main advantages and disadvantages of clustering algorithms for dealing with big data in a scalable approach behind the different other features.

An Adaptive Parameter-Free Optimal Number of Market Segments Estimation Algorithm Based on a New Internal Validity Index

“…In the literature, there are two main types of clustering algorithms that are often combined with IVIs to determine the ONC: hierarchical and partitional clustering [8]. For the hierarchical clustering, according to the direction of clustering, there are two types of methods: agglomerative hierarchical clustering (AHC) and divisive hierarchical clustering (DHC) [21], where the former follows the bottom-top strategy, which treats each sample as a complete cluster at the beginning, and then gradually merges them into some larger cluster based on a certain criterion, and on the contrary, the DHC adopts the top-down strategy, which initially regards the entire dataset as a complete cluster and then splits the dataset into some smaller clusters based on a certain criterion [22]. Compared with the DHC, the AHC is more accurate and widely used [23], and in recent years, the classic AHC with single linkage, complete linkage, average linkage and ward linkage are still the most widely used AHC methods [24,25].…”

Data clustering: application and trends

“…This is the size of input data. The size of data was presented as a factor that affects the selection of clustering algorithm by Andreopoulos et al ( 2009 ), Shirkhorshidi et al ( 2014 ) and more recently Mahdi et al ( 2021 ). They observed that some clustering algorithms perform poorly and sacrifice quality when the size of data increases in volume, velocity, variability and variety.…”

“…Aggarwal et al ( 2003 ) described data stream as large volumes of data arriving at an unlimited growth rate. As noted by Mahdi et al ( 2021 ) data types that are vast and complex to store such as social network data (referred to as big data) and high-speed data (data stream) such as web-click streams, network traffic could be challenging to cluster. In addition, they emphasized that the type of data type considered often influences the type of clustering techniques selected.…”

A New Density Peak Clustering Algorithm With Adaptive Clustering Center Based on Differential Privacy

“…Clustering analysis is an important part of data mining and the basis of some data mining methods [3]. Its application scenarios are very wide, in computer science [4], [5], [6], biological [7], [8], chemistry [9], society [10] and…”