Since time series clustering is performed without prior information, it is used for exploratory data analysis.In particular, clusters of time series with similar shapes can be used in various fields, such as business, medicine, finance, and communications.However, existing time series clustering algorithms have a problem in that time series with different shapes are included in the clusters.The reason for such a problem is that the existing algorithms do not consider the limitations on the size of the generated clusters, and use a dimension reduction method in which the information loss is large.In this paper, we propose a method to alleviate the disadvantages of existing methods and to find a better quality of cluster containing similarly shaped time series.In the data preprocessing step, we normalize the time series using z-transformation.Then, we use piecewise aggregate approximation (PAA) to reduce the dimension of the time series.In the clustering step, we use density-based spatial clustering of applications with noise (DBSCAN) to create a precluster.We then use a modified K-means algorithm to refine the preclusters containing differently shaped time series into subclusters containing only similarly shaped time series.In our experiments, our method showed better results than the existing method.

Jungyu Ahn

Ju-Hong Lee

KSII Transactions on Internet and Information Systems

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS (TIIS) 是被 SCIE (THOMSON REUTERS) 和 SCOPUS (ELSEVIER) 检索的在线学术期刊，由 KSII 出版并得到 KETI 的支持。交易每隔一个月发布一次。交易旨在让读者在与有线和无线互联网和信息系统相关的多个重点领域获得最先进的技术。 IT 的技术和应用正在迅速变化和更新。因此，向研究人员、开发人员、部署工程师、技术经理和教育工作者快速发布和分发至关重要。我们最重要的目标是在收到稿件后迅速发表被录用的论文。

KSII Transactions on Internet and Information Systems (TIIS) is an online academic journal published by KSII and supported by KETI, indexed by SCIE (THOMSON REUTERS) and SCOPUS (ELSEVIER). The journal is published bi-monthly and aims to provide readers with cutting-edge technologies related to wired and wireless internet and information systems. The technologies and applications in IT are rapidly evolving, making it essential to quickly publish and disseminate research findings for researchers, developers, deployment engineers, technical managers, and educators. Our primary goal is to swiftly publish accepted papers upon receipt of submissions.

KSII Transactions on Internet and Information Systems (TIIS) 是一本由 KSII 出版并得到 KETI 支持的在线学术期刊，已被 SCIE (THOMSON REUTERS) 和 SCOPUS (ELSEVIER) 检索。该期刊每隔一个月发布一次，旨在为读者提供与有线和无线互联网及信息系统相关的前沿技术。信息技术的技术和应用正在迅速变化，因此，快速发布和传播研究成果对研究人员、开发人员、部署工程师、技术经理和教育工作者至关重要。我们的主要目标是在收到稿件后迅速发表被录用的论文。

Clustering Algorithm for Time Series with Similar Shapes

This paper proposes a method to alleviate the disadvantages of existing methods and to find a better quality of cluster containing similarly shaped time series, which shows better results than the existing method.

Category	Quartile
计算机科学	4
计算机科学, 计算机信息系统	4
计算机科学, 电信学	4

Clustering Algorithm for Time Series with Similar Shapes

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