2016
DOI: 10.1108/ijicc-01-2016-0004
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Evaluation of employee profiles using a hybrid clustering and optimization model

Abstract: Purpose The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers. Employee profiles play a crucial role in the evaluation process to improve the training process performance. This paper focuses on the clustering of the employees based on their profiles into specific categories that represent the employees’ characteristics. The employees are classified into following categories: necessary training, required training, and… Show more

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Cited by 4 publications
(2 citation statements)
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“…Clustering algorithm has been widely used in many business applications such as clustering employee profile for providing a suitable training program for the employee (Esmaeilzadeh et al, 2016), clustering operational managerial research (Brusco et al, 2017), clustering online learning resources (Q. Wu et al, 2016), clustering customer behavior of bank's customers (Abbasimehr & Shabani, 2019), investigating user's search experience and satisfaction (Burt & Liew, 2012), clustering railway driving mission (Yatchev et al, 2012), evaluating the use of intellectual intelligence tools (Fourati-Jamoussi et al, 2018), etc.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Clustering algorithm has been widely used in many business applications such as clustering employee profile for providing a suitable training program for the employee (Esmaeilzadeh et al, 2016), clustering operational managerial research (Brusco et al, 2017), clustering online learning resources (Q. Wu et al, 2016), clustering customer behavior of bank's customers (Abbasimehr & Shabani, 2019), investigating user's search experience and satisfaction (Burt & Liew, 2012), clustering railway driving mission (Yatchev et al, 2012), evaluating the use of intellectual intelligence tools (Fourati-Jamoussi et al, 2018), etc.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In a data clustering problem we are presented with a set of data points that must be grouped according to notion of similarity (Wagstaff et al, 2001). Data clustering is widely used in various real-world applications such as business and marketing (Esmaeilzadeh et al, 2016), data/text/web mining (Wang and Zhang, 2017), healthcare (Gebremeskel et al, 2016), social science and so on. Data clustering is fundamentally done with some initial assumptions such as distance metric, data structure, number of clusters, data distribution, etc.…”
Section: Introductionmentioning
confidence: 99%