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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.13254 |
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| _version_ | 1866911514480345088 |
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| author | Sylvestre, Marie-Pierre Boulanger, Laurence |
| author_facet | Sylvestre, Marie-Pierre Boulanger, Laurence |
| contents | We present a new algorithm for clustering longitudinal data. Data of this type can be conceptualized as consisting of individuals and, for each such individual, observations of a time-dependent variable made at various times. Generically, the specific way in which this variable evolves with time is different from one individual to the next. However, there may also be commonalities; specific characteristic features of the time evolution shared by many individuals. The purpose of the method we put forward is to find clusters of individual whose underlying time-dependent variables share such characteristic features. This is done in two steps. The first step identifies each individual to a point in Euclidean space whose coordinates are determined by specific mathematical formulae meant to capture a variety of characteristic features. The second step finds the clusters by applying the Spectral Clustering algorithm to the resulting point cloud. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13254 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Introducing Feature-Based Trajectory Clustering, a clustering algorithm for longitudinal data Sylvestre, Marie-Pierre Boulanger, Laurence Machine Learning Computation We present a new algorithm for clustering longitudinal data. Data of this type can be conceptualized as consisting of individuals and, for each such individual, observations of a time-dependent variable made at various times. Generically, the specific way in which this variable evolves with time is different from one individual to the next. However, there may also be commonalities; specific characteristic features of the time evolution shared by many individuals. The purpose of the method we put forward is to find clusters of individual whose underlying time-dependent variables share such characteristic features. This is done in two steps. The first step identifies each individual to a point in Euclidean space whose coordinates are determined by specific mathematical formulae meant to capture a variety of characteristic features. The second step finds the clusters by applying the Spectral Clustering algorithm to the resulting point cloud. |
| title | Introducing Feature-Based Trajectory Clustering, a clustering algorithm for longitudinal data |
| topic | Machine Learning Computation |
| url | https://arxiv.org/abs/2603.13254 |