Classification of temporal sequences using rough clustering
Classification of temporal sequences using rough clustering
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DOI：
10.1109/nafips.2004.1337389
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发表时间：
2004-06
期刊：
影响因子：
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通讯作者：
S. Hirano;S. Tsumoto
中科院分区：
文献类型：
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作者：
S. Hirano;S. Tsumoto
本文提出了一种比较研究的方法聚类长期的时态数据。我们将聚类过程分为两个过程：相似性计算和分组。作为相似性计算方法，我们采用动态时间规整（DTW）和多尺度匹配。作为分组方法，我们采用了传统的凝聚层次聚类（AHC）和粗糙集聚类（RC）。使用这些方法的各种组合，我们进行了聚类实验的肝炎数据集，并评估结果的有效性。结果表明：（1）完全连锁（CL）标准比平均连锁（AL）标准对聚类结果的解释能力更强;（2）DTW和CL-AHC的结合能不断产生可解释的结果;（3）DTW和RC的结合能找到聚类的核心序列，（4）多尺度匹配可能会受到“不匹配”对的处理的影响，然而，通过使用RC作为后续分组方法可以避免该问题。
This paper presents a comparative study of methods for clustering long-term temporal data. We split a clustering procedure into two processes: similarity computation and grouping. As similarity computation methods, we employed dynamic time warping (DTW) and multiscale matching. As grouping methods, we employed conventional agglomerative hierarchical clustering (AHC) and rough sets-based clustering (RC). Using various combinations of these methods, we performed clustering experiments of the hepatitis data set and evaluated validity of the results. The results suggested that (1) complete-linkage (CL) criterion outperformed average-linkage (AL) criterion in terms of the interpret-ability of a dendrogram and clustering results, (2) combination of DTW and CL-AHC constantly produced interpretable results, (3) combination of DTW and RC would be used to find the core sequences of the clusters, (4) multiscale matching may suffer from the treatment of 'no-match' pairs, however, the problem may be eluded by using RC as a subsequent grouping method.