Mapping Student Performance With Employment Using Fuzzy C-Means

Technical organisations are ranked based on performance indicators like resources, students' intake, global reputation, and research activities. Student performance and placement are important factors in deciding the ranking of a university. Student performance analysis is a recent and widely researched domain aimed at reforming the education system. The analysis assists institutions to understand and improve their performance and educational outcomes. Admissions, academics, and placement are the three most significant processes during which the large amount of data is gathered within a university and there is a requirement of analysis. The data mining techniques are used for data analysis processes and it encompasses data understanding, pre-processing, modelling, and implementation. In this research work, fuzzy c-means clustering technique is used to understand fuzziness of student performance, classify and map the student performance to employability. To understand this objective, the dataset has been collected from universities, pre-processed, and analysed.