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Reseach Article

Applicability of Clustering and Classification Algorithms for Recruitment Data Mining

by K. Ramar, N. Sivaram
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 5
Year of Publication: 2010
Authors: K. Ramar, N. Sivaram
10.5120/823-1165

K. Ramar, N. Sivaram . Applicability of Clustering and Classification Algorithms for Recruitment Data Mining. International Journal of Computer Applications. 4, 5 ( July 2010), 23-28. DOI=10.5120/823-1165

@article{ 10.5120/823-1165,
author = { K. Ramar, N. Sivaram },
title = { Applicability of Clustering and Classification Algorithms for Recruitment Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { July 2010 },
volume = { 4 },
number = { 5 },
month = { July },
year = { 2010 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume4/number5/823-1165/ },
doi = { 10.5120/823-1165 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:52:17.914681+05:30
%A K. Ramar
%A N. Sivaram
%T Applicability of Clustering and Classification Algorithms for Recruitment Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 4
%N 5
%P 23-28
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recruitment of appropriate employees and their retention are the major concerns towards creating the competitive strength in the knowledge economy. Every year IT companies recruit fresh graduates through their campus selection programs after examining their skills by conducting tests, group discussion and a number of interviews. The recruitment process requires enormous amount of effort and investment. During each phase of the recruitment process, candidates are filtered based on some performance criteria. Intense analysis on the system indicates that a pattern exists among the candidates selected for an industry. The problem domain is complex and the aspects of candidates that impact the recruitment process is not explicit. In this research, the domain knowledge is extracted through knowledge acquisition techniques. Data mining techniques that fit the problem are determined. A study has been made by applying K-means and fuzzy C-means clustering and decision tree classification algorithms to the recruitment data of an industry. Experiments were conducted with the data collected from an IT industry to support their hiring decisions. Pruned and unpruned trees were constructed using ID3, C4.5 and CART algorithms. From the comparative study, it has been observed that the clustering algorithms are not much suitable for the problem and performance of the C4.5 decision tree algorithm is high. Using the constructed decision trees, discussions were made with the domain experts to deduce viable decision rules.

References
Index Terms

Computer Science
Information Sciences

Keywords

Recruitment process Knowledge Engineering Classification Decision trees Fuzzy C-means algorithm K-means algorithm