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Soft Computing

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09.06.2018 | Methodologies and Application

Hierarchical clustering of unequal-length time series with area-based shape distance

verfasst von: Xiao Wang, Fusheng Yu, Witold Pedrycz, Jiayin Wang

Erschienen in: Soft Computing | Ausgabe 15/2019

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Abstract

Time-series clustering algorithms have been used in a variety of areas to extract valuable information from complex and massive data sets. However, these algorithms suffer from two shortcomings. On the one hand, most of them are designed for the equal-length time series, while clustering of unequal-length time series is often encountered in real-world problems. On the other hand, commonly used distance measures of time series cannot fully reveal trend differences. To overcome these two shortcomings, this paper focuses on the trend of time series and employs the area-based shape distance to measure their similarity. In addition, we present a new hierarchical clustering for unequal-length time series based on area-based shape distance measure. A series of experiments illustrates the performance of the proposed clustering algorithm.

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Titel: Hierarchical clustering of unequal-length time series with area-based shape distance
verfasst von: Xiao Wang
Fusheng Yu
Witold Pedrycz
Jiayin Wang
Publikationsdatum: 09.06.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 15/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3287-6

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