DED: Drift Principle in Educational Evolved Data

Abstract

Clustering data streams is one of the prominent tasks of discovering hidden patterns in data streams. It refers to the process of clustering newly arrived data into continuously and dynamically changing segmentation patterns. This article presents a stream mining algorithm to cluster the data stream with focusing on its evolution and concept drift. Even though concept drift is expected to be present in data streams, explicit drift detection is rarely done in stream clustering algorithms. Concept drift is caused by the changes in data distribution over time. Relationship between concept drift and the occurrence of physical events has been studied by applying the algorithm on the education data stream. Viber education data streams produced by Viber Groups in our Computer Science Department are used to conduct this study. The results show that our proposed algorithm superiority existing ones in purity, entropy, and sum of square error measurements. Experiments led to the conclusion that the concept drift accompanied by a change in the number of clusters and outliers indicates a significant education event. This kind of online monitoring and its results can be utilized in education systems in various ways, such as present the capabilities of participants.