Application of classification technique of data mining for employee management system

This paper presents the application of classification technique of data mining used for the Employee Management System (EMS). This paper discusses the classification techniques of data mining and based on the data, the process of Knowledge Discovery in Databases (KDD) is reformed for classifying large data into different categories such as Disability, Employee Performance, etc. This paper discusses, WEKA data mining toolkit classifier model to predict employee’s performance based on the employee’s age, date of joining and number of years of experience. This study helps to predict the employee’s work-cycle and helps the management to find the employee’s performance those who are disabled and enabled. The paper addresses the system to get the details of those employees who need special attention and guide the management to make policies to improve employees’ performance. We demonstrate the application in a real-life situation. © Springer International Publishing AG, part of Springer Nature 2018.

• Autores: J. Kamatkar, Sadhana; Tayade, Amarapali; Viloria Silva, Amelec Jesus; Hernandez Chacin, Ana Emilia
• Tipo de recurso: http://purl.org/coar/resource_type/c_f744
• Fecha de publicación: 2018
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/1560
• Acceso en línea: https://hdl.handle.net/11323/1560; https://repositorio.cuc.edu.co/
• Palabra clave: Classification; Data mining; Data searching; Employee details; Employee Management System (EMS)
• Rights: openAccess
• License: Atribución – No comercial – Compartir igual