Abstract
With the advance in both hardware and software technologies, automated data generation and storage has become faster than ever. Such data is referred to as data streams. Streaming data is ubiquitous today and it is often a challenging task to store, analyze and visualize such rapid large volumes of data. Most conventional data mining techniques have to be adapted to run in a streaming environment, because of the underlying resource constraints in terms of memory and running time. Furthermore, the data stream may often show concept drift, because of which adaptation of conventional algorithms becomes more challenging. One such important conventional data mining problem is that of classification. In the classification problem, we attempt to model the class variable on the basis of one or more feature variables. While this problem has been extensively studied from a conventional mining perspective, it is a much more challenging problem in the data stream domain. In this chapter, we will re-visit the problem of classification from the data stream perspective. The techniques for this problem need to be thoroughly re-designed to address the issue of resource constraints and concept drift. This chapter reviews the state-of-the-art techniques in the literature along with their corresponding advantages and disadvantages.
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Gaber, M.M., Zaslavsky, A., Krishnaswamy, S. (2007). A Survey of Classification Methods in Data Streams. In: Aggarwal, C.C. (eds) Data Streams. Advances in Database Systems, vol 31. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-47534-9_3
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DOI: https://doi.org/10.1007/978-0-387-47534-9_3
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