A review on data stream classification approaches

Sajad Homayoun, Marzieh Ahmadzadeh
2016 Journal of Advanced Computer Science & Technology  
<p>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
more » ... ods 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.</p>
doi:10.14419/jacst.v5i1.5225 fatcat:p6uhbyskwjbdpc5xoezpznctrq