A SYSTEMATIC REVIEW ON EXPLORING THE ROLE OF CLUSTERING ALGORITHMS IN EDUCATIONAL DATA MINING

Abstract

The review examines the objectives, datasets, algorithms, and application areas of studies, with specific attention to student performance analysis, learning conduct identification, personalized learning, and educational decision support systems. The outcomes reveal that K-means is the most used clustering algorithm due to its ease and effectiveness, while tools such as Weka, SPSS, and learning analytics platforms are commonly employed. The study features the efficacy of clustering techniques in segmenting students, predicting academic performance, and supporting instructional strategies. This exam specifies effective perceptions into current trends, trials, and future directions in applying clustering algorithms within EDM, highlighting their prospective to enhance educational characteristic and understanding experiences. The review examines the objectives, datasets, algorithms, and application areas of studies, with specific attention to student performance analysis, learning conduct identification, personalized learning, and educational decision support systems. The outcomes reveal that K-means is the most used clustering algorithm due to its ease and effectiveness, while tools such as Weka, SPSS, and learning analytics platforms are commonly employed. The goal of data mining is to extract knowledge from massive amounts of data. Among the data mining algorithms are clustering, regression, summarization, association rules, and detection of irregularities. A variety of private and open-source data mining tools are used. Using the MCLUST method, predict a system for educational data mining using ensemble and filtering techniques. The results show that the e-learning style algorithm is the most widely employed algorithm. An automatic comparing method using spectral clustering, OPTICS, TOPSIS, k-medoids, and BSCAN. This work conducts the same experiment on a controlled version of the datasets based on the findings. To use data mining to examine pupils' accomplishments, CGPA was used to create groups using the K-means algorithm and to use data clustering and predictive modeling to track pupils' progress.