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Knowledge Discovery Techniques in Human Talent Management: “A Knowledge Discovery Databases Approach to Conquer Employee Attrition Problem Using Data Mining Techniques”

K. P. Tripathi1 , Ashutosh Gaur2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 517-522, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.517522

Online published on Jan 31, 2019

Copyright © K. P. Tripathi, Ashutosh Gaur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: K. P. Tripathi, Ashutosh Gaur, “Knowledge Discovery Techniques in Human Talent Management: “A Knowledge Discovery Databases Approach to Conquer Employee Attrition Problem Using Data Mining Techniques”,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.517-522, 2019.

MLA Style Citation: K. P. Tripathi, Ashutosh Gaur "Knowledge Discovery Techniques in Human Talent Management: “A Knowledge Discovery Databases Approach to Conquer Employee Attrition Problem Using Data Mining Techniques”." International Journal of Computer Sciences and Engineering 7.1 (2019): 517-522.

APA Style Citation: K. P. Tripathi, Ashutosh Gaur, (2019). Knowledge Discovery Techniques in Human Talent Management: “A Knowledge Discovery Databases Approach to Conquer Employee Attrition Problem Using Data Mining Techniques”. International Journal of Computer Sciences and Engineering, 7(1), 517-522.

BibTex Style Citation:
@article{Tripathi_2019,
author = {K. P. Tripathi, Ashutosh Gaur},
title = {Knowledge Discovery Techniques in Human Talent Management: “A Knowledge Discovery Databases Approach to Conquer Employee Attrition Problem Using Data Mining Techniques”},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {517-522},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3535},
doi = {https://doi.org/10.26438/ijcse/v7i1.517522}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.517522}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3535
TI - Knowledge Discovery Techniques in Human Talent Management: “A Knowledge Discovery Databases Approach to Conquer Employee Attrition Problem Using Data Mining Techniques”
T2 - International Journal of Computer Sciences and Engineering
AU - K. P. Tripathi, Ashutosh Gaur
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 517-522
IS - 1
VL - 7
SN - 2347-2693
ER -

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Abstract

Talent resource management is one of the complex tasks for human resource professionals to assign the right person for the right place at the right time in the organization. The sustainability of suitable employee in an organization is very crucial these days. In this competitive age, employees are switching the organization on some gain but the organization suffers a lot. This paper is mainly concerned with the application of the knowledge discovery technique in human resource management, particularly in talent resource management to conquer the employee attrition and predicting the possible attrition in future.

Key-Words / Index Term

Soft Computing, Fuzzy logic, Decision Tree, Talent Management, KDD, Knowledge Discovery

References

[1] A. S. Chang, & Leu, S.S., "Data mining model for identifying project profitablility variables," International Journal of Project Management, vol. 24, pp. 199-206, 2006.
[2] A TP Track Research Report "Talent Management: A State of the Art," Tower Perrin HR Services 2005.
[3] E. Frank, Hall, M., et al., "Data mining in bioinformatics using Weka," Bioinformatics Application Note, vol. 20, pp. 2479-2481, 2004.
[4] H. Jantan, A. R. Hamdan, Z. A. Othman, and M. Puteh, "Applying Data Mining Classification Techniques for Employee`s Performance Prediction," in Knowledge Management 5th International Conference (KMICe2010), Kuala Terengganu, Terengganu Malaysia, 2010, pp. 645-652.
[5] I. Bose, & Mahapatra, R.K., "Business data mining - a machine learning perspective " Information & Management, vol. 39, pp. 211-225, 2001.
[6] Jantan, H., Hamdan, A. R., & Othman, Z. A. (2010). Human talent prediction in HRM using C4. 5 classification algorithm, International Journal on Computer Science and Engineering, 2(08-2010), pp 2526-2534
[7] Jantan H., Hamdan A.R., Othman Z.A. (2009) Classification Techniques for Talent Forecasting in Human Resource Management. In: Huang R., Yang Q., Pei J., Gama J., Meng X., Li X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science, vol 5678. Springer, Berlin, Heidelberg
[8] J. Hamidah, H. Abdul Razak, and A. O. Zulaiha, "Classification for Talent Management using Decision Tree Induction Techniques," in 2nd Data Mining and Optimization Seminar (DMO’09), Bangi, Selangor, 2009, pp. 15-20.
[9] Kurgan, L.A., Musilek, P. (2006). A survey of knowledge discovery and Data Mining Models, The Knowledge Engineering Review, 21(1), pp 1 – 24
[10] Phyu, T.N., (2009). Survey of classification techniques in data mining, Proceedings of the International Multi Conference Of Engineers And Computer Scientists, IMECS 2009, Vol 1
[11] S. H. Liao, Chen, Y.N., & Tseng, Y.Y., "Mining demand chain knowledge of life insurance market for new product development," Expert Systems with Applications, vol. 36, pp. 9422-9437, 2009.
[12] V. Cho, & Ngai, E.W.T., "Data mining for selection of insurance sales agents," vol. 20, pp. 123-132, 2003