Concept Drift Detection on Data Stream for Revising DBSCAN Cluster
Concept Drift Detection on Data Stream for Revising DBSCAN Cluster
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DOI：
10.1145/3405962.3405990
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Yasushi Miyata;H. Ishikawa
中科院分区：
文献类型：
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作者：
Yasushi Miyata;H. Ishikawa
物联网数据的数据流挖掘可以帮助运营商立即隔离设备告警的原因。然而，挑战在于如何保持分类器的高纯度（即将同一类的数据保持在正确的聚类中），同时处理由于警报模型和实体之间的差异而导致的概念漂移。我们建议根据数据分布和趋势变化不断修正分类模型。评价表明，振荡条件下的数据在漂移率为1%时没有纯度下降。结果表明，我们的方法可以帮助作业者改善决策。
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.