Analysis of Simple K-Mean and Parallel K-Mean Clustering for Software Products and Organizational Performance Using Education Sector Dataset

Abstract

Context. Educational Data Mining (EDM) is a new and emerging research area. Data mining techniques are used in the educational field in order to extract useful information on employee or student progress behaviors. Recent increase in the availability of learning data has given importance and momentum to educational data mining to better understand and optimize the learning process and the environments in which it takes place. Objective. Data are the most valuable commodity for any organization. It is very difficult to extract useful information from such a large and massive collection of data. Data mining techniques are used to forecast and evaluate academic performance of students based on their academic record and participation in the forum. Although several studies have been carried out to evaluate the academic performance of students worldwide, there is a lack of appropriate studies to assess factors that can boost the academic performance of students. Methodology. The current study sought to weigh up factors that contribute to improving student academic performance in Pakistan. In this paper, both the simple and parallel clustering techniques are implemented and analyzed to point out their best features. The Parallel K-Mean algorithms overcome the problems of simple algorithm and the outcomes of the parallel algorithms are always the same, which improves the cluster quality, number of iterations, and elapsed time. Results. Both the algorithms are tested and compared with each other for a dataset of 10,000 and 5000 integer data items. The datasets are evaluated 10 times for minimum elapse time-varying K value from 1 to 10. The proposed study is more useful for scientific research data sorting. Scientific research data statistics are more accurate.