Open AccessData Mining Models of High Dimensional Data Streams, and Contemporary Concept Drift Detection Methods: a Comprehensive Review
Author(s) -
M Sankara Prasanna Kumar,
Ajit Kumar,
K. Prasanna
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.6.14959
Subject(s) - data stream mining , concept drift , streams , computer science , data mining , change detection , identification (biology) , data stream , streaming data , data science , artificial intelligence , telecommunications , computer network , biology , botany
Concept drift is defined as the distributed data across multiple data streams that change over the time. Concept drift is visible only when the type of collected data changes after some stable period. The emergence of concept drift in data streams leads to increase misclassification and performing degradation of data streams. In order to obtain accurate results, identification of such concept drifts must be visible. This paper focused on a review of the issues related to identifying the changes occurred in the various multivariate high dimensional data streams. The insight of the manuscript is probing the inbuilt difficulties of existing contemporary change-detection methods when they encounter during data dimensions scales.