A Review on Data Stream Classification

• Main Authors: A. A., Haneen, Noraziah, Ahmad, Mohd Helmy, Abd Wahab
• Format: Conference or Workshop Item
• Language: English
• Published: IOP Publishing 2018
• Subjects: Z665 Library Science. Information Science
• Online Access: http://umpir.ump.edu.my/id/eprint/24879/1/Haneen_2018.pdf; http://umpir.ump.edu.my/id/eprint/24879/; https://doi.org/10.1088/1742-6596/1018/1/012019

At this present time, the significance of data streams cannot be denied as many researchers have placed their focus on the research areas of databases, statistics, and computer science. In fact, data streams refer to some data points sequences that are found in order with the potential to be non-binding, which is generated from the process of generating information in a manner that is not stationary. As such the typical tasks of searching data have been linked to streams of data that are inclusive of clustering, classification, and repeated mining of pattern. This paper presents several data stream clustering approaches, which are based on density, besides attempting to comprehend the function of the related algorithms; both semi-supervised and active learning, along with reviews of a number of recent studies.