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A Practical Approach for Emails Multiclass Classification according to Senders using Naïve Bayers Technique

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
G. Girija Rani, M. Indra Sena Reddy
10.5120/ijca2016910399

Girija G Rani and Indra Sena M Reddy. A Practical Approach for Emails Multiclass Classification according to Senders using Naïve Bayers Technique. International Journal of Computer Applications 144(6):34-37, June 2016. BibTeX

@article{10.5120/ijca2016910399,
	author = {G. Girija Rani and M. Indra Sena Reddy},
	title = {A Practical Approach for Emails Multiclass Classification according to Senders using Naïve Bayers Technique},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {144},
	number = {6},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {34-37},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume144/number6/25186-2016910399},
	doi = {10.5120/ijca2016910399},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Emails are parts of everyday life. These messages have become increasingly important and widespread method of communication because of its time speed, where the amount of email messages received per day can range from tens for a regular user to thousands for companies. Everyone is overwhelmed with emails, including relational (structured) and non-relational (semi-structured or non-structured), quite a bit of which is repetitive, stale and of drastically differing quality. This large quantity is confounded. Not just spam messages are thought to be 'garbage', additionally undesirable messages (e.g. advertisements, lottery) individuals’ waste a lot of time unknowingly by surfing them. So there is much need to categorization of Emails. Classification can help to meet lawful and administrative necessities for recovering particular data inside of a set time span, and this is frequently the inspiration driving implementing data classification. This paper aims at examining on ways doing supervised and unsupervised grouping of messages as per email content.

References

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Keywords

Supervised, unsupervised, classification.