Data mining techniques for better decisions in human resource management systems

Ranjan, Jayanthi; Goyal, D.P.; Ahson, S. I.

doi:10.1504/ijbis.2008.018597

Cited by 60 publications

(49 citation statements)

References 30 publications

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“…Ranjan presented the Data Mining approach to discover and extract useful patterns from this large data set to find observable patterns in HR and thus improve the quality of the decision-making process in HRM [15]. Jantan et al demonstrated how clustering, classification and association rules can be applied to solve modern Talent Management challenges such as detecting employees with similar characteristics, predicting the employee performance and associating the employee's profile to the most appropriate job [8].…”

Section: Related Workmentioning

confidence: 99%

Improving Talent Management with Automated Competence Assessment: Research Summary

Nikitinsky¹

2017

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

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Improvement and automatization of Human Resource Management processes is an aspiring trend in modern business. In this paper, we summarize the results of the research we conducted in order to create a formal Text Mining-based competence assessment model and develop a decision support system for talent management (DSSTM) based on this model. We also conducted several experiments in order to improve the performance of the competence assessment model. The resulting prototype of DSSTM currently undergoes validation in a software development company and shows promising results.

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Section: Related Workmentioning

confidence: 99%

Improving Talent Management with Automated Competence Assessment: Research Summary

Nikitinsky¹

2017

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

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“…Data mining tasks are generally categorized as clustering, association, classification and prediction (Chien & Chen, 2008;Ranjan, 2008). Over the years, data mining has evolved various techniques to perform the tasks that include database oriented techniques, statistic, machine learning, pattern recognition, neural network, rough set and etc.…”

Section: Classification and Prediction In Data Miningmentioning

confidence: 99%

“…Recently, there are some researches that show great interest on solving HR problems using data mining approach (Ranjan, 2008). Table 1 lists some of the tasks in human resource that use data mining technique, and it shows there are quite limited studies on data mining in human resource domain.…”

Section: Fig 1 Classification and Prediction In Data Miningmentioning

confidence: 99%

“…Recently, with the new demand and increased visibility, HR seeks a more strategic role by turning to data mining methods (Ranjan, 2008). This can be done by discovering generated patterns as useful knowledge from the existing data in HR databases.…”

Section: Human Talent Forecastingmentioning

confidence: 99%

“…There are many areas which adapted this approach to solve their problems such as in finance, medical, marketing, stock, telecommunication, manufacturing, health care, customer relationship and etc. However, the data mining application has not attracted much attention from people in Human Resource (HR) field (Chien & Chen, 2008;Ranjan, 2008). Besides that, in our previous study, most of the prediction applications are used to predict stock, demand, rate, risk, event and others; but there are quite limited studies on human prediction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Mining Classification Techniques for Human Talent Forecasting

Jantan¹,

Hamdan²,

Othman³

2011

Knowledge-Oriented Applications in Data Mining

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Training older workers: Lessons learned, unlearned, and relearned from the field of instructional design

Rooij

2012

Human Resource Management

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Changing workforce demographics have highlighted the need to provide training and development opportunities for older workers. This article critically examines the current state of research on the use of systematic instructional design procedures to develop work‐related training that is inclusive of older workers. The review reveals a disconnect between what the literature recommends to achieve age‐inclusive design and what is offered as evidence of age‐inclusive design. Age‐related generalizations about cognition and learning capacity often serve as the basis for design decisions, with little attention paid to the role of training context and content. The article recommends that this disconnect be resolved and offers some specific suggestions about how human resource managers can integrate sound instructional design principles into the training and development component of their talent management strategies. © 2012 Wiley Periodicals, Inc.

show abstract

Data mining techniques for better decisions in human resource management systems

Cited by 60 publications

References 30 publications

Improving Talent Management with Automated Competence Assessment: Research Summary

Improving Talent Management with Automated Competence Assessment: Research Summary

Data Mining Classification Techniques for Human Talent Forecasting

Training older workers: Lessons learned, unlearned, and relearned from the field of instructional design

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