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首页> 外文会议>Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining(PAKDD 2005); 20050518-20; Hanoi(VN) >A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams
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A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams

机译：基于MPAA的迭代聚类算法，并通过最近邻搜索来增强时间序列数据流

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摘要

In streaming time series the Clustering problem is more complex, since the dynamic nature of streaming data makes previous clustering methods inappropriate. In this paper, we propose firstly a new method to evaluate Clustering in streaming time series databases. First, we introduce a novel multi-resolution PAA (MPAA) transform to achieve our iterative clustering algorithm. The method is based on the use of a multi-resolution piecewise aggregate approximation representation, which is used to extract features of time series. Then, we propose our iterative clustering approach for streaming time series. We take advantage of the multiresolution property of MPPA and equip a stopping criteria based on Hoeffding bound in order to achieve fast response time. Our streaming time-series clustering algorithm also works by leveraging off the nearest neighbors of the incoming streaming time series datasets and fulfill incremental clustering approach. The comprehensive experiments based on several publicly available real data sets shows that significant performance improvement is achieved and produce high-quality clusters in comparison to the previous methods.
机译：在流时间序列中，聚类问题更加复杂，因为流数据的动态性质使以前的聚类方法不合适。在本文中，我们首先提出了一种评估流时间序列数据库中聚类的新方法。首先，我们介绍一种新颖的多分辨率PAA（MPAA）变换，以实现我们的迭代聚类算法。该方法基于多分辨率分段聚合近似表示的使用，该表示用于提取时间序列的特征。然后，我们提出了用于流时间序列的迭代聚类方法。我们利用MPPA的多分辨率属性，并基于Hoeffding界限配备了停止条件，以实现快速响应时间。我们的流式时间序列聚类算法还可以通过利用传入的流式时间序列数据集的最近邻居来实现增量聚类方法。基于几个可公开获得的真实数据集的综合实验表明，与以前的方法相比，可以显着提高性能并生成高质量的群集。

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