A Systematic Review of Density Grid-Based Clustering for Data Streams

Abstract

Various applications, such as electronic business, satellite remote sensing, intrusion discovery, and network traffic monitoring, generate large unbounded data stream sequences at a rapid pace. The clustering of data streams has attracted considerable interest due to the increasing usage of evolving data streams. In particular, evolving data streams affect clustering because they introduce numerous challenges, such as time and memory limits and one-pass clustering. Furthermore, researchers need to be able to determine arbitrarily shaped clusters present in evolving data streams from applications. Due to these characteristics, conventional density grid-based clustering techniques cannot be used. Moreover, the existing density grid-based clustering algorithms have low cluster quality for clustering evolving data streams. This study conducted a systematic literature review (SLR) and noted numerous research-related issues encountered in solving the aforementioned problems. We summarized numerous grid-based clustering algorithms that have been used and determined their distinctive and limited features. We also observed how these algorithms address the challenges affecting the clustering of evolving data streams and studied their advantages and disadvantages. SLR was based on 104 articles published between 2010 and 2021. Numerous challenges remain for grid-based clustering algorithms, particularly in terms of time-limited and high-dimensional data handling. Last, our findings indicated a variety of active studies on density grid-based clustering.