Applicability of Clustering and Classification Algorithms for Recruitment Data Mining

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.

Toward a Good Decision to Improve the Weight of Control of Expenditure for Local Communities

“…That is why we introduce a segmentation technique (Sivaram and Ramar, 2010;Antonellis et al, 2007;Riad Solh and El Belkacemi, 2015) to group and classify LC who share the same level of revenue ratios (Fig. 1).…”

“…In order to make a relevant and accurate classification, we segmented first the LC (Sivaram and Ramar, 2010;Antonellis et al, 2007;Riad Solh and El Belkacemi, 2015) according to their local revenue ratios (MDFEDLP, 2004;Riad Solh and El Belkacemi, 2015) by producing a set of clusters (Shih et al, 2010;Ayesha et al, 2010;Ivancsy and Kovacs, 2006;Riad Solh and El Belkacemi, 2015) (Fig. 2).…”

Assessing Personal Performance with M-SVMs

“…In particularly, a quickly growing number of research contributions have been paid to the application of data mining techniques in supporting the various HRM activities and processes, such as employee selection [4][5], staffing analysis for turnover [6][7][8] and evaluating employee performance in the function of performance management [9][10]. On the stream of techniques used to support the above HRM works, decision trees [11][12][13] (Sivaram & Ramar, 2010), support vector machines [14] and neural nets [15] have been widely employed. The readers are recommended to refer to a recent literature review [16] for more details.…”

An Approach to Determine Non-filer's of Property Tax using Clustering and Classification

“…Ramar, use engineering data sets and the input attributes to determine through knowledge engineering in an IT industry. The process involves defining the problem, identifying relevant stake holders, and learns about current explanation to the problem [5].…”

“…Clustering and classification are the mostly used methods of data mining. Clustering can be used for define and decision tree can be applied to analyzing [5]. [3].…”