EMPLOYEE PERFORMANCE APPRAISAL SYSTEM BASED ON RANKING AND REVIEWS


Ashish Modi, Sharath Kumar J, Sharath Kumar J, Sharath Kumar J, Muralidhar A, Muralidhar A, Muralidhar A

Abstract


Objective: In many organizations, employee data have to be maintained and utilized for many purposes. Here, in this paper, we are going to use such data to calculate an employee’s performance.

Methods: This employee data may be converted into useful information using data mining techniques such as K-means and decisions tree. K-means is used to find the rank of the employee means that the employee may come under in his criteria. Decision tree is used to find the review of an employee means that the employee needs improvement or he/she meets expectation.

Results: This algorithm when utilized can identify the top employee who can be considered for appraisal or the eligible candidates for promotion. Hence, these algorithms such as K-mean and decision tree that help to find best employees for any association and help us to take a good decision in less time.

Conclusion: There are various factors which should be considered and are limited to this algorithm, so human intervention is required to consider those factors. However, ranking and appraisal are seen in many companies, and this algorithm will definitely identify the potential candidates.


Keywords


Employee performance, Clustering, Decision tree, K-means, Employee performance, Data mining, Euclidean distance.

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References


Kumar SA, Vijayalakshmi MN. Mining of student academic evaluation records in higher education. In: Recent Advances in Computing and Software Systems (RACSS), 2012 International Conference on IEEE; 2012. p. 67-70.

Geng X, Luo L. Multilabel ranking with inconsistent rankers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2014. p. 3742-7.

Bouhmala N. How good is the Euclidean distance metric for the clustering problem. In: Advanced Applied Informatics (IIAI-AAI), 2016 5th IIAI International Congress on IEEE; 2016. p. 312-5.

Esteves RM, Hacker T, Rong C. Competitive k-means, a new accurate and distributed k-means algorithm for large datasets. In: Cloud Computing Technology and Science (Cloud Com), 2013 IEEE 5th International Conference on IEEE. Vol. 1; 2013. p. 17-24.

Kumar KM, Reddy AR. A fast K-means clustering using prototypes for initial cluster center selection. In: Intelligent Systems and Control (ISCO), 2015 IEEE 9th International Conference on IEEE; 2015. p. 1-4.

Poteraş CM, Mocanu ML. Evaluation of an optimized K-means algorithm based on real data. In: Computer Science and Information Systems (Fed CSIS), 2016 Federated Conference on IEEE; 2016. p. 831-5. 7. Kotalwar R, Gandhi S, Chavan R. Data mining: Evaluating performance of employee’s using classification algorithm based on decision tree. Eng Sci Technol Int J 2014;4:29-35.

Yang Y, Chen W. Taiga: Performance optimization of the C4. 5 decision tree construction algorithm. Tsinghua Sci Technol 2016;21(4):415-25.

Guleria P, Thakur N, Sood M. Predicting student performance using decision tree classifiers and information gain. In: Parallel, Distributed and Grid Computing (PDGC), 2014 International Conference on IEEE; 2014. p. 126-9.

Vaidya J, Shafiq B, Fan W, Mehmood D, Lorenzi D. A random decision tree framework for privacy-preserving data mining. IEEE Trans Dependable Secure Comput 2014;11(5):399-411.

Lin C, Du X, Jiang X, Wang D. An efficient and effective performance estimation method for DSE. In: VLSI Design, Automation and Test (VLSI-DAT), 2016 International Symposium on IEEE; 2016. pp. 1-4.

Chen Q, Gong Z. Data mining modelling of employee engagement for it enterprises based on decision tree algorithm. In: Information Management, Innovation Management and Industrial Engineering (ICIII), 2013 6th International Conference on IEEE. Vol. 2; 2013. p. 305-8.




About this article

Title

EMPLOYEE PERFORMANCE APPRAISAL SYSTEM BASED ON RANKING AND REVIEWS

Keywords

Employee performance, Clustering, Decision tree, K-means, Employee performance, Data mining, Euclidean distance.

DOI

10.22159/ajpcr.2017.v10s1.23489

Date

01-04-2017

Additional Links

Manuscript Submission

Journal

Asian Journal of Pharmaceutical and Clinical Research
Special Issue April 2017 Page: 495-498

Print ISSN

0974-2441

Online ISSN

2455-3891

Authors & Affiliations

Ashish Modi
Department of , School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
India

Sharath Kumar J
Department of , School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
India

Sharath Kumar J
Department of , School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
India

Sharath Kumar J
Department of , School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
India

Muralidhar A
Department of , School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
India

Muralidhar A
Department of , School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
India

Muralidhar A
Department of , School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
India


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