A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

被引:630
|
作者
Fahad, Adil [1 ,4 ]
Alshatri, Najlaa [1 ]
Tari, Zahir [1 ]
Alamri, Abdullah [1 ]
Khalil, Ibrahim [1 ]
Zomaya, Albert Y. [2 ]
Foufou, Sebti [3 ]
Bouras, Abdelaziz [3 ]
机构
[1] RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic 3000, Australia
[2] Univ Sydney, Sch Informat Technol, Ctr Distributed & High Performance Comp, Sydney, NSW 2006, Australia
[3] Qatar Univ, Dept Comp Sci, Coll Engn, Doha 2713, Qatar
[4] Al Baha Univ, Dept Comp Sci, Al Baha City 65431, Saudi Arabia
关键词
Clustering algorithms; unsupervised learning; big data;
D O I
10.1109/TETC.2014.2330519
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.
引用
收藏
页码:267 / 279
页数:13
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