Comparative Study of Gaussian and Nearest Mean Classifiers for Filtering Spam E-mails

Dr. Upasna Attri; Harpreet Kaur

Comparative Study of Gaussian and Nearest Mean Classifiers for Filtering Spam E-mails

Dr. Upasna Attri * §

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Harpreet Kaur

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§ Punjab Technical University, Jalandhar (India)

Comparative Study of Gaussian and Nearest Mean Classifiers for Filtering Spam E-mails

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Abstract

The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. The article gives an overview of some of the most popular machine learning methods (Gaussian and Nearest Mean) and of their applicability to the problem of spam e-mail filtering. The aim of this paper is to compare and investigate the effectiveness of classifiers for filtering spam e-mails using different matrices. Since spam is increasingly becoming difficult to detect, so these automated techniques will help in saving lot of time and resources required to handle e-mail messages.

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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

Dr. Upasna Attri, Harpreet Kaur. 2012. "Comparative Study of Gaussian and Nearest Mean Classifiers for Filtering Spam E-mails". Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 12 (GJCST Volume 12 Issue E11).

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GJCST Volume 12 Issue E11

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Classifiers Data mining Filtering Machine Learning spam E-mails

Classification

GJCST-E Classification H.1.0

Submission ReceivedDecember 9, 2011
Peer Review Double Blind
Handling Editor
Accepted January 3, 2012
Published January 15, 2012

Version of record

v1.2

Issue date

July 10, 2012

Language

English

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Article Matrices

Total Score: 147

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Dr. Upasna Attri, Harpreet Kaur (PhD/Dr. count: 1)

View Count (all-time): 397

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Publish Date: 2012 01, Sun

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Total Views: 2.3K

Total Downloads: 185

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