Clustering of time series data - a survey
Clustering of time series data - a survey
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DOI：
10.1016/j.patcog.2005.01.025
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发表时间：
2005-11-01
影响因子：
8
通讯作者：
Liao, TW
中科院分区：
文献类型：
--
作者：
Liao, TW
时间序列聚类已被证明是有效的，在各个领域提供有用的信息。作为时态数据挖掘研究的一部分，时间序列聚类似乎越来越受到人们的关注。为了提供一个概述，本文调查和总结以前的工作，调查了聚类的时间序列数据在各个应用领域。时间序列聚类的基础知识，包括通用的聚类算法，通常用于时间序列聚类研究，用于评估聚类结果的性能的标准，以及措施，以确定两个时间序列之间的相似性/相异性进行比较，无论是在原始数据的形式，提取的功能，或一些模型参数。过去的研究被分为三组，这取决于他们是否直接使用原始数据（时域或频域），间接使用从原始数据中提取的特征，或间接使用从原始数据中构建的模型。讨论了以往研究的独特性和局限性，并指出了未来研究的几个可能的主题。此外，时间序列聚类的应用领域也进行了总结，包括所使用的数据来源。希望这篇评论将成为那些有兴趣推进这一研究领域的人的垫脚石。(c)2005模式识别学会。由爱思唯尔有限公司出版。保留所有权利。
Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.