Downloads: 1 | Views: 57 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1
Informative Article | Science and Technology | India | Volume 10 Issue 10, October 2021
The Data Mining Techniques for Analyzing Employee Performance and Productivity
Abstract: Increasing employee performance and productivity has become an essential objective for contemporary businesses, given the frequently changing nature of the terrain in which they operate. This is necessary for sustaining competitiveness and attaining sustainable growth. Data mining methods provide companies with useful instruments for extracting insights from the large amounts of employee-related data that are accumulated over the course of their operations. The purpose of this study is to provide an overview of the many data mining approaches that have been used to examine the performance and productivity of employees. This section of the research will begin by elaborating on the relevance of employee performance evaluations and the association between those evaluations and the success of the business as a whole. After that, it enters into the world of data mining and explains fundamental ideas like the preparation of data, the selection of features, and the assessment of models. An in-depth examination of data mining methods, including both supervised and unsupervised learning strategies, is presented in the following paragraphs. Techniques of supervised learning, including decision trees, support vector machines, and neural networks, are investigated in the context of their use to forecast the performance of employees and determine the elements that influence productivity. The purpose of this study is to examine unsupervised approaches such as clustering and association rule mining in order to find hidden patterns within employee data. This will make it easier to identify employee groups that have different performance characteristics.
Keywords: Employee Performance, Human Resource Analytics, Data Analytics
Edition: Volume 10 Issue 10, October 2021,
Pages: 1575 - 1578