A fast K-Means clustering using prototypes for initial cluster center selection

Kumar, Kanike Vinod; Reddy, A. Rama Mohan

doi:10.1109/isco.2015.7282319

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…To use K, silhouette measures are used. The cluster can be ranked or classified to get an enhanced student performance [5,6]. This algorithm may help to get a student performance result in a ranking manner [7].…”

Section: Literature Surveymentioning

confidence: 99%

Employee Performance Appraisal System Based on Ranking and Reviews

Modi

Muralidhar

2017

Asian J Pharm Clin Res

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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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Section: Literature Surveymentioning

confidence: 99%