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Concept drift detection on stream data for revising DBSCAN
Author(s) -
Miyata Yasushi,
Ishikawa Hiroshi
Publication year - 2021
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.12288
Subject(s) - concept drift , operator (biology) , computer science , data stream , data mining , class (philosophy) , constant false alarm rate , data stream mining , dbscan , cluster (spacecraft) , artificial intelligence , cluster analysis , computer network , fuzzy clustering , telecommunications , biochemistry , chemistry , repressor , transcription factor , gene , canopy clustering algorithm
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.