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篇名 |
Concept Drift Detection Based on Pre-Clustering and Statistical Testing
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並列篇名 | Concept Drift Detection Based on Pre-Clustering and Statistical Testing |
作者 | Jones Sai-Wang Wan、Sheng-De Wang |
英文摘要 | Stream data processing has become an important issue in the last decade. Data streams are generated on the fly and possibly change their data distribution over time. Data stream processing requires some mechanisms or methods to adapt to the changes of data distribution, which is called the concept drift. Concept drift detection can be challenging due to the data labels are not known. In this paper, we propose a drift detection method based on the statistical test with clustering and feature extraction as preprocessing. The goal is to reduce the detection time with principal component analysis (PCA) for the feature extraction method. Experimental results on synthetic and real-world streaming data show that the clustering preprocessing improve the performance of the drift detection and feature extraction trade-off an insignificant performance of detection for speedup for the execution time. |
起訖頁 | 465-472 |
關鍵詞 | Concept drift、Stream data mining、Drift detection、Unsupervised |
刊名 | 網際網路技術學刊 |
期數 | 202103 (22:2期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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