Concept Drift Detection in Data Stream Mining: The Review of Contemporary Literature

B. Ramakrishna
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Concept Drift Detection in Data Stream Mining: The Review of Contemporary Literature

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Background

Abstract

Mining process such as classification, clustering of progressive or dynamic data is a critical objective of the information retrieval and knowledge discovery; in particular, it is more sensitive in data stream mining models due to the possibility of significant change in the type and dimensionality of the data over a period. The influence of these changes over the mining process termed as concept drift. The concept drift that depict often in streaming data causes unbalanced performance of the mining models adapted. Hence, it is obvious to boost the mining models to predict and analyse the concept drift to achieve the performance at par best. The contemporary literature evinced significant contributions to handle the concept drift, which fall in to supervised, unsupervised learning, and statistical assessment approaches. This manuscript contributes the detailed review of the contemporary concept-drift detection models depicted in recent literature. The contribution of the manuscript includes the nomenclature of the concept drift models and their impact of imbalanced data tuples.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

B. Ramakrishna. 2017. "Concept Drift Detection in Data Stream Mining: The Review of Contemporary Literature". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 17 (GJCST Volume 17 Issue C2).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
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GJCST-C Classification C.1.1
H.2.8
  • Submission ReceivedDecember 10, 2016
  • Peer Review Double Blind
    Handling Editor
  • Accepted January 3, 2017
  • Published January 15, 2017
Version of record

v1.2

Issue date
August 5, 2017

Language
English
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Concept Drift Detection in Data Stream Mining: The Review of Contemporary Literature

B. Ramakrishna
B. Ramakrishna JNTU
  • Submission ReceivedDecember 10, 2016
  • Peer Review Double Blind
    Handling Editor
  • Accepted January 3, 2017
  • Published January 15, 2017