A Systematic Review of Learning based Notion Change Acceptance Strategies for Incremental Mining

A Systematic Review of Learning based Notion Change Acceptance Strategies for Incremental Mining

D.S.S.K.Dhanalakshmi

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Dr. Ch.Suneetha

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Jawaharlal Nehru Technological University, Hyderabad

A Systematic Review of Learning based Notion Change Acceptance Strategies for Incremental Mining

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Abstract

The data generated contemporarily from different communication environments is dynamic in content different from the earlier static data environments. The high speed streams have huge digital data transmitted with rapid context changes unlike static environments where the data is mostly stationery. The process of extracting, classifying, and exploring relevant information from enormous flowing and high speed varying streaming data has several inapplicable issues when static data based strategies are applied. The learning strategies of static data are based on observable and established notion changes for exploring the data whereas in high speed data streams there are no fixed rules or drift strategies existing beforehand and the classification mechanisms have to develop their own learning schemes in terms of the notion changes and Notion Change Acceptance by changing the existing notion, or substituting the existing notion, or creating new notions with evaluation in the classification process in terms of the previous, existing, and the newer incoming notions. The research in this field has devised numerous data stream mining strategies for determining, predicting, and establishing the notion changes in the process of exploring and accurately predicting the next notion change occurrences in Notion Change.

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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.

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How to Cite This Article

D.S.S.K.Dhanalakshmi. 2016. \u201cA Systematic Review of Learning based Notion Change Acceptance Strategies for Incremental Mining\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C2).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-C Classification H.2.8

D.3.4

D.2.3

Submission ReceivedDecember 6, 2015
Peer Review Double Blind
Handling Editor
Accepted January 1, 2016
Published January 15, 2016

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v1.2

Issue date

May 31, 2016

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Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: D.S.S.K.Dhanalakshmi, Dr. Ch.Suneetha (PhD/Dr. count: 1)

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Total Views: 7350

Total Downloads: 1894

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