Time Series Overlapping Clustering Based on Link Community Detection
Given the nature of time series and their vast applications, it is essential to find clustering algorithms that depict their real-life properties. Among the features that can hugely effect the options available for time series are overlapping and hierarchical properties. In this paper a novel approa...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10473004/ |
| _version_ | 1797243450650787840 |
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| author | Yasamin Ghahremani Babak Amiri |
| author_facet | Yasamin Ghahremani Babak Amiri |
| author_sort | Yasamin Ghahremani |
| collection | DOAJ |
| description | Given the nature of time series and their vast applications, it is essential to find clustering algorithms that depict their real-life properties. Among the features that can hugely effect the options available for time series are overlapping and hierarchical properties. In this paper a novel approach to analyze time series with such features is introduced. Using the two concepts of network construction and link community detection, we have attempted to analyze and identify the mentioned properties of time series using data that is often gathered first hand. The proposed algorithm has been applied using both recent and common similarity measures on ten synthetic time series with hierarchal and overlapping features, alongside various distance measures. When testing the proposed approach, the element-centric measure of similarity indicated a clear increased accuracy for this algorithm, showing the highest accuracy when used alongside the Dynamic Time Warping distance measure. Moreover, the proposed algorithm has been very successful in identifying and forming communities for both large and small time series, thus solving another one of the main issues previous algorithms tended to have. |
| first_indexed | 2024-04-24T18:55:19Z |
| format | Article |
| id | doaj.art-8151353b70ac413f9a9669bafc10158a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:55:19Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8151353b70ac413f9a9669bafc10158a2024-03-26T17:43:40ZengIEEEIEEE Access2169-35362024-01-0112411024112410.1109/ACCESS.2024.337765610473004Time Series Overlapping Clustering Based on Link Community DetectionYasamin Ghahremani0Babak Amiri1https://orcid.org/0000-0001-9469-5648School of Industrial Engineering, Iran University of Science and Technology, Tehran, IranSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranGiven the nature of time series and their vast applications, it is essential to find clustering algorithms that depict their real-life properties. Among the features that can hugely effect the options available for time series are overlapping and hierarchical properties. In this paper a novel approach to analyze time series with such features is introduced. Using the two concepts of network construction and link community detection, we have attempted to analyze and identify the mentioned properties of time series using data that is often gathered first hand. The proposed algorithm has been applied using both recent and common similarity measures on ten synthetic time series with hierarchal and overlapping features, alongside various distance measures. When testing the proposed approach, the element-centric measure of similarity indicated a clear increased accuracy for this algorithm, showing the highest accuracy when used alongside the Dynamic Time Warping distance measure. Moreover, the proposed algorithm has been very successful in identifying and forming communities for both large and small time series, thus solving another one of the main issues previous algorithms tended to have.https://ieeexplore.ieee.org/document/10473004/Network sciencecommunity detectiontime seriesmachine learningdynamic time warping |
| spellingShingle | Yasamin Ghahremani Babak Amiri Time Series Overlapping Clustering Based on Link Community Detection IEEE Access Network science community detection time series machine learning dynamic time warping |
| title | Time Series Overlapping Clustering Based on Link Community Detection |
| title_full | Time Series Overlapping Clustering Based on Link Community Detection |
| title_fullStr | Time Series Overlapping Clustering Based on Link Community Detection |
| title_full_unstemmed | Time Series Overlapping Clustering Based on Link Community Detection |
| title_short | Time Series Overlapping Clustering Based on Link Community Detection |
| title_sort | time series overlapping clustering based on link community detection |
| topic | Network science community detection time series machine learning dynamic time warping |
| url | https://ieeexplore.ieee.org/document/10473004/ |
| work_keys_str_mv | AT yasaminghahremani timeseriesoverlappingclusteringbasedonlinkcommunitydetection AT babakamiri timeseriesoverlappingclusteringbasedonlinkcommunitydetection |