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Cluster based Ranking Index for Enhancing Recruitment Process using Text Mining and Machine Learning

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Mayuri Verma
10.5120/ijca2017912812

Mayuri Verma. Cluster based Ranking Index for Enhancing Recruitment Process using Text Mining and Machine Learning. International Journal of Computer Applications 157(9):23-30, January 2017. BibTeX

@article{10.5120/ijca2017912812,
	author = {Mayuri Verma},
	title = {Cluster based Ranking Index for Enhancing Recruitment Process using Text Mining and Machine Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {9},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {23-30},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume157/number9/26860-2017912812},
	doi = {10.5120/ijca2017912812},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

This paper presents an effective approach for extracting relevant words from the resumes using Term Document Matrix. The role of the candidate, various skills, familiarity with various frameworks, experienced skills and operating systems have been considered. A clustering methodology has been used to find the similar resumes. The importance of each word has been calculated according to the cluster which makes this paper unique. The appropriate rank of the resumes have been calculated. The experimental results shows that Cluster Based Ranking gives the potentially best candidate for a particular job profile. The weighted importance in calculating the ranks is the very first effort in itself. Further work can be done in this area for improving the productivity in the recruitment process.

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Keywords

Resume, K Means, ReliefF