Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyse 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 analyse and categorise 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) datasets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime and scalability tests. Additionally, we highlighted the set of clustering algorithms that are the best performing for big data.
Clustering algorithms, Algorithm design and analysis, Partitioning algorithms, Big data, Clustering methods, Neural networks, Taxonomies,
Abdelaziz Bouras, "A Survey of Clustering Algorithms for Big Data: Taxonomy & Empirical Analysis", IEEE Transactions on Emerging Topics in Computing, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TETC.2014.2330519