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.

中文翻译：

---

对流数据进行概念漂移检测以修订DBSCAN

物联网数据的数据流挖掘可以支持运营商立即隔离设备警报的原因。但是，挑战在于保持其分类器的高纯度（群集中具有相同适当类的数据比率），而概念漂移则归因于警报模型和实体之间的差异。我们建议根据其分布变化不断更新数据类。通过评估，对于漂移条件为1％的振荡条件数据，未确认纯度下降。结果表明，该方法可以提高操作者的决策能力。