A survey on data stream clustering and classification

Nowadays, with the advance of technology, many applications generate huge amounts of data streams at very high speed. Examples include network traffic, web click streams, video surveillance, and sensor networks. Data stream mining has become a hot research topic. Its goal is to extract hidden knowledge/patterns from continuous data streams. Unlike traditional data mining where the dataset is static and can be repeatedly read many times, data stream mining algorithms face many challenges and have to satisfy constraints such as bounded memory, single-pass, real-time response, and concept-drift detection. This paper presents a comprehensive survey of the state-of-the-art data stream mining algorithms with a focus on clustering and classification because of their ubiquitous usage. It identifies mining constraints, proposes a general model for data stream mining, and depicts the relationship between traditional data mining and data stream mining. Furthermore, it analyzes the advantages as well as limitations of data stream algorithms and suggests potential areas for future research.