A review on data stream classification approaches

Abstract

Stream data is usually in vast volume, changing dynamically, possibly infinite, and containing multi-dimensional features. The attention towards data stream mining is increasing as regards to its presence in wide range of real-world applications, such as e-commerce, banking, sensor data and telecommunication records. Similar to data mining, data stream mining includes classification, clustering, frequent pattern mining etc. techniques; the special focus of this paper is on classification methods invented to handle data streams. Early methods of data stream classification needed all instances to be labeled for creating classifier models, but there are some methods (Semi-Supervised Learning and Active Learning) in which unlabeled data is employed as well as labeled data. In this paper, by focusing on ensemble methods, semi-supervised and active learning, a review on some state of the art researches is given.