Concept Drift Detection on Data Stream for Revising DBSCAN Cluster

Abstract

Data stream mining of IoT data can help operators immediately isolate causes of equipment alarms. The challenge, however, is how to keep the classifiers high-purity (i.e., keep data of the same class in the right cluster) while dealing with the concept drifting ascribed to differences between alarm models and entities. We propose continuously revising the classification model in accordance with the data distribution and trend changes. Evaluations showed there was no purity deterioration for oscillation condition data with a drifting rate of 1%. This result demonstrates that our approach can help operators improve their decision making.