Concept drift detection on stream data for revising DBSCAN

Abstract

Data stream mining of IoT data can support operator to immediately isolate causes of equipment alarms. The challenge, however, is to keep their classifiers high purity (the data ratio with same proper class in a cluster) with concept drifting ascribed to differences between alarm models and entities. We propose to continuously update data class according to their distribution changes. Through evaluation, no purity deterioration was verified for oscillation condition data with a drifting rate of 1%. The result suggested that the method improves operator decision making.