A taxonomy and empirical analysis of clustering algorithms for traffic classification

Abstract

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 have 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 created a confusion amongst the 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 chapter introduces concepts and algorithms related to clustering, a concise survey existing (clustering) algorithms as well as providing a comparison both from a theoretical and empirical perspective. From a theoretical perspective, we come up with a categorizing framework based on the main properties pointed out in previous study. Empirically, extensive experiments are carried out 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. Additionally, we highlighted the set of clustering algorithms that are the best performing for big data.