Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

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

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

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

	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 = {},
	doi = {10.5120/ijca2017912812},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


  5. Yu, Kun, Gang Guan, and Ming Zhou. "Resume information extraction with cascaded hybrid model." Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2005.
  7. Kopparapu, Sunil Kumar. "Automatic extraction of usable information from unstructured resumes to aid search." Progress in Informatics and Computing (PIC), 2010 IEEE International Conference on. Vol. 1. IEEE, 2010.
  8. Zhi Xiang Jiang, Chuang Zhang, Bo Xiao, Zhiqing Lin, “Research and Implementation of Intelligent Chinese Resume Parsing”, WRI International Conference on Communications and Mobile Computing, Jan 2009.
  9. Zhang Chuang, Wu Ming, Li Chun Guang, Xiao Bo, “Resume Parser: Semi-structured Chinese Document Analysis”, WRI World Congress on Computer Science and Information Engineering, April 2009.
  10. Celik Duygu, Karakas Askyn, Bal Gulsen, Gultunca Cem, “Towards an Information Extraction System Based on Ontology to Match Resumes and Jobs”, IEEE 37th Annual Workshops on Computer Software and Applications Conference Workshops, July 2013.
  11. Feldman, Ronen, and James Sanger. The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, 2007.
  12. Manning, Christopher D., and Hinrich Schütze. Foundations of statistical natural language processing. Vol. 999. Cambridge: MIT press, 1999.
  15. Hartigan, John A., and Manchek A. Wong. "Algorithm AS 136: A k-means clustering algorithm." Journal of the Royal Statistical Society. Series C (Applied Statistics) 28.1 (1979): 100-108.
  16. Tibshirani, Robert, Guenther Walther, and Trevor Hastie. "Estimating the number of clusters in a data set via the gap statistic." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63.2 (2001): 411-423.
  17. Liu, Huan, and Hiroshi Motoda, eds. Computational methods of feature selection. CRC Press, 2007.


Resume, K Means, ReliefF