Data Stream Clustering: A Review

TL;DR

This paper reviews data stream clustering techniques, discussing concepts, challenges, algorithms, and tools, providing a comprehensive overview of current methods and open problems in real-time data stream analysis.

Contribution

It offers a detailed survey of recent data stream clustering algorithms, analyzing their techniques, complexities, and accuracies, and highlights open challenges and datasets.

Findings

Comparison of clustering algorithms based on technique and accuracy

Identification of open problems in data stream clustering

Overview of tools and datasets for data stream analysis

Abstract

Number of connected devices is steadily increasing and these devices continuously generate data streams. Real-time processing of data streams is arousing interest despite many challenges. Clustering is one of the most suitable methods for real-time data stream processing, because it can be applied with less prior information about the data and it does not need labeled instances. However, data stream clustering differs from traditional clustering in many aspects and it has several challenging issues. Here, we provide information regarding the concepts and common characteristics of data streams, such as concept drift, data structures for data streams, time window models and outlier detection. We comprehensively review recent data stream clustering algorithms and analyze them in terms of the base clustering technique, computational complexity and clustering accuracy. A comparison of these algorithms is given along with still open problems. We indicate popular data stream repositories and datasets, stream processing tools and platforms. Open problems about data stream clustering are also discussed.