Concept drift detection on stream data for revising DBSCAN

被引：0

作者：

Miyata Y. ^{[1
]}

Ishikawa H. ^{[2
]}

机构：

[1] Hitachi, Ltd., Research and Development Group, 1-280, Higashi-koigakubo, Kokubunji, Tokyo

[2] Tokyo Metropolitan University, 6-6, Asahigaoka, Hino, Tokyo

来源：

IEEJ Transactions on Electronics, Information and Systems | 2020年 / 140卷 / 08期

关键词：

Clustering; Concept drift; Data stream mining; DBSCAN; Power grid;

D O I：

10.1541/ieejeiss.140.949

中图分类号：

学科分类号：

摘要：

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. © 2020 The Institute of Electrical Engineers of Japan.

引用

页码：949 / 955

页数：6

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