Graphint: Graph-based Time Series Clustering Visualisation Tool
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912268663390208 |
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| author | Boniol, Paul Tiano, Donato Bonifati, Angela Palpanas, Themis |
| author_facet | Boniol, Paul Tiano, Donato Bonifati, Angela Palpanas, Themis |
| contents | With the exponential growth of time series data across diverse domains, there is a pressing need for effective analysis tools. Time series clustering is important for identifying patterns in these datasets. However, prevailing methods often encounter obstacles in maintaining data relationships and ensuring interpretability. We present Graphint, an innovative system based on the $k$-Graph methodology that addresses these challenges. Graphint integrates a robust time series clustering algorithm with an interactive tool for comparison and interpretation. More precisely, our system allows users to compare results against competing approaches, identify discriminative subsequences within specified datasets, and visualize the critical information utilized by $k$-Graph to generate outputs. Overall, Graphint offers a comprehensive solution for extracting actionable insights from complex temporal datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graphint: Graph-based Time Series Clustering Visualisation Tool Boniol, Paul Tiano, Donato Bonifati, Angela Palpanas, Themis Machine Learning With the exponential growth of time series data across diverse domains, there is a pressing need for effective analysis tools. Time series clustering is important for identifying patterns in these datasets. However, prevailing methods often encounter obstacles in maintaining data relationships and ensuring interpretability. We present Graphint, an innovative system based on the $k$-Graph methodology that addresses these challenges. Graphint integrates a robust time series clustering algorithm with an interactive tool for comparison and interpretation. More precisely, our system allows users to compare results against competing approaches, identify discriminative subsequences within specified datasets, and visualize the critical information utilized by $k$-Graph to generate outputs. Overall, Graphint offers a comprehensive solution for extracting actionable insights from complex temporal datasets. |
| title | Graphint: Graph-based Time Series Clustering Visualisation Tool |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.07698 |