Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning
Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning
复制标题
通过将多个距离测量与半监督学习相结合来增强时间序列聚类
DOI：
10.1007/s11390-015-1565-7
复制
发表时间：
2015-07
影响因子：
0.7
通讯作者：
Zhang Yanchun
中科院分区：
文献类型：
--
作者：
Zhou Jing;Zhu Shan-Feng;Huang Xiaodi;Zhang Yanchun
时间序列聚类在各个领域有着广泛的应用。现有的研究主要集中在两个时间序列之间的距离度量，如基于动态时间规整（DTW）的方法，编辑距离的方法，基于shapelets的方法。在这项工作中，我们实验证明，第一次，没有一个单一的距离测量显着优于其他聚类数据集的时间序列谱聚类。因此，出现了一个问题，如何选择一个适当的措施，为一个给定的数据集的时间序列。为了回答这个问题，我们提出了一个集成方案，采用半监督聚类的多个距离措施。我们的方法是能够集成所有的措施，提取有价值的底层信息的聚类。据我们所知，这项工作首次表明，基于约束的半监督聚类方法能够通过结合多种距离度量来增强时间序列聚类。在对各种时间序列数据集进行聚类测试后，我们证明了我们的方法优于单独的措施，以及典型的集成方法。
Time series clustering is widely applied in various areas. Existing researches focus mainly on distance measures between two time series, such as dynamic time warping (DTW) based methods, edit-distance based methods, and shapelets-based methods. In this work, we experimentally demonstrate, for the first time, that no single distance measure performs significantly better than others on clustering datasets of time series where spectral clustering is used. As such, a question arises as to how to choose an appropriate measure for a given dataset of time series. To answer this question, we propose an integration scheme that incorporates multiple distance measures using semi-supervised clustering. Our approach is able to integrate all the measures by extracting valuable underlying information for the clustering. To the best of our knowledge, this work demonstrates for the first time that the semi-supervised clustering method based on constraints is able to enhance time series clustering by combining multiple distance measures. Having tested on clustering various time series datasets, we show that our method outperforms individual measures, as well as typical integration approaches.
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一种新颖的时间序列数据聚类方法
影响因子：
8.5
作者：
Zhang, Xiaohang;Liu, Jiaqi;Du, Yu;Lv, Tingjie
通讯作者：
Lv, Tingjie
DOI：
10.1007/978-981-4585-18-7_28
发表时间：
2013
期刊：
--
影响因子：
--
作者：
Swee Chuan Tan;Jess Pei San Lau
通讯作者：
Swee Chuan Tan;Jess Pei San Lau
影响因子：
4.8
作者：
Wang, Xiaozhe;Smith, Kate;Hyndman, Rob
通讯作者：
Hyndman, Rob
DOI：
10.1007/s11280-013-0256-y
发表时间：
2013-10
期刊：
World Wide Web
影响因子：
--
作者：
Jianzhong Qi;Rui Zhang;K. Ramamohanarao;Hongzhi Wang;Zeyi Wen;Dan Wu
通讯作者：
Jianzhong Qi;Rui Zhang;K. Ramamohanarao;Hongzhi Wang;Zeyi Wen;Dan Wu
DOI：
--
发表时间：
2009-02
期刊：
World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering
影响因子：
--
作者：
R. Ghaemi;M. N. Sulaiman;H. Ibrahim;N. Mustapha
通讯作者：
R. Ghaemi;M. N. Sulaiman;H. Ibrahim;N. Mustapha