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Quantifying Performance Appraisal Parameters: A Forward Feature Selection Approach
Objectives: The objective of the paper is study and select optimal set of parameters present in performance appraisal (PA). It will result into the best PA. For achieving this, the technique used is "feature selection" and "clustering" and it is supported with the data analytical tool "R". Methods/Statistical Analysis: The paper covers the data mining analysis. For achieving this, the technique used is "feature selection" and "clustering" and it is supported with the data analytical tool "R". Findings: This paper focuses on performance appraisal, the multiple parameters available; to be exact it is 34 parameters. To select set of parameters, 13 from the set of 34, which when focused by the employees can have optimum PA. For achieving this, the technique used is "feature selection" and "clustering" and it is supported with the data analytical tool "R". Applications/Improvements: Usable for every firm where employees have PA. In Organizations today, Human resource and performance appraisal has become very crucial. This is significant from the perception of both the management and the employees. The mechanism of measuring the performance appraisal has also evolved over a period. Recently there are multiple factors and parameters which are taken for measuring the performance appraisal of an individual employee. This complete process is very challenging for both, the employer and the employee. The employer i.e. the organization comes up with different mechanisms and keeps abreast with changing scenarios in order to be competitive in the industry. It is the employees who face the major difficulties in understanding and deciding what to address in their day to day work, which can get appreciation from the employers. Ultimately a good appraisal is every employee's desire
Clustering, Data Analytical Tool R, Feature Selection, Performance Appraisal.
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