动态特征空间中的时态数据挖掘

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI:10.1109/ICDM.2006.157

B. Wenerstrom, C. Giraud-Carrier

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引用次数: 43

摘要

时间数据挖掘的许多有趣的实际应用程序都受到概念漂移的阻碍。概念漂移的一种特殊形式是以底层特征空间的变化为特征的。在这方面似乎做得很少。本文提出了FAE，一种增量集成方法来挖掘受这种概念漂移影响的数据。大数据流的实证结果显示了前景。

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Temporal Data Mining in Dynamic Feature Spaces

Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done in this area. This paper presents FAE, an incremental ensemble approach to mining data subject to such concept drift. Empirical results on large data streams demonstrate promise.

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Sixth International Conference on Data Mining (ICDM'06)

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