A relative study of clustering data mining in the education sector of Bangladesh

Clustering Data mining is extracting knowledge from data using data analysis tools to conduct pattern recognition and predictive modeling tasks. It is a common technique for statistical data, machine learning, and computer-science analysis. Clustering is a kind of unsupervised data mining technique that describes general working behavior and pattern extraction and extracts valuable information from electricity price time series. This article explains data mining technology to analyze educational data from Bangladesh universities' using various types of clustering data mining techniques- Partitioning Clustering, Hierarchical Clustering, Grid-based clustering, Model-based clustering, and Density-based clustering. The event logs acquired from the natural e-course e-learning environment were used for the analysis. This investigation used cluster analysis and decision trees as data mining approaches. Cluster analysis divided the students into groups based on their behavior when using the material. It was a method that I was interested in generating a decision expression that could define a class of objects for a detailed analysis of how the decision tree students learned.