EMPLOYEE PERFORMANCE APPRAISAL SYSTEM BASED ON RANKING AND REVIEWS
DOI:
https://doi.org/10.22159/ajpcr.2017.v10s1.23489Keywords:
Employee performance, Clustering, Decision tree, K-means, Data mining, Euclidean distanceAbstract
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.
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