Dataset Generation and Cluster Creation for Adaptive E-Learning System Using the Rectilinear Technique of K-Means Clustering, Demonstrated Using Java

Data is an essential element in research which can be challenging to obtain especially when such data is termed classified. Consequently, researchers depend on dataset or direct collection from respondents. The volume of data collected through this means is grossly limited and laborious putting into consideration, the resources involved in the collection and accuracy rate. In order to ease this, researches that requires demonstration, can rely on internally synthetic generated data. This work looks at how data can be generated using Java multi-dimensional array, and the classification of generated data into cluster using the k-means rectilinear technique, that is used to classify adaptiveness of learners in an eLearning environment. With the combination of simple and complex codes, the work adequately and accurately generated 125 elements, created 5 clusters based on the fusion of known and adopted learning pedagogies, which can be used to determine how learners learn different subject matters.