Data Stream Classification Techniques to Find Novel Class – A Review
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 6
Abstract
Data Stream Mining (DSM) is the way of retrieving useful knowledge from fast information records. Today, there are number of application that produce massive amount of stream data. Concept evolution, the phenomenon of class disappearance and emergence is a valuable research topic in data stream mining field. Concept evolution happens when a new class occurs in the stream. Similar to data mining, data stream mining includes classification and clustering techniques; the special focus of this paper is on classification techniques developed to handle data streams. The problem of classification is one of the most widely studied in the context of data stream mining. The problem of classification is made more difficult by the evolution of the underlying data stream. Based on the efficiency of the classification technique used, the accuracy of classification varies. This paper reviews different classification techniques for handling the novel class in the data streams.
Authors and Affiliations
Aswathi R Kaimal, Joshy Thomas Jose
Keywords
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