WAT: Finding Top-K Discords in Time Series Database.
作者： Yingyi Bu , Jian Pei , Ada Wai-Chee Fu , Eamonn J. Keogh , Oscar Tat-Wing Leung
DOI:
关键词: Pruning (decision trees) 、 Wavelet 、 Wavelet packet decomposition 、 Stationary wavelet transform 、 Discrete wavelet transform 、 Time series database 、 Wavelet transform 、 Data mining 、 Haar wavelet 、 Computer science
摘要: Finding discords in time series database is an important problem a great variety of applications, such as space shuttle telemetry, mechanical industry, biomedicine, and financial data analysis. However, most previous methods for this suffer from too many parameter settings which are difficult users. The best known approach to our knowledge that has comparatively fewer parameters still requires users choose word size the compression subsequences. In paper, we propose Haar wavelet augmented trie based algorithm mine top-K database, can dynamically determine compression. Due characteristics transform, greater pruning power than approaches. Through experiments with some annotated datasets, effectiveness efficiency both attested.
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