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Evolutionary Clustering of Streaming Trajectories
Databases · Submitted 23 Sep 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Extensive experiments with two real-life datasets offer insight into ECO and show that it outperforms state-of-the-art solutions in terms of both clustering quality and efficiency.
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The abstract, sentence by sentence
The widespread deployment of smartphones and location-enabled, networked in-vehicle devices renders it increasingly feasible to collect streaming trajectory data of moving objects. The continuous clustering of such data can enable a variety of real-time services, such as identifying representative paths or common moving trends among objects in real-time. However, little attention has so far been given to the quality of clusters -- for example, it is beneficial to smooth short-term fluctuations in clusters to achieve robustness to exceptional data. We propose the notion of evolutionary clustering of streaming trajectories, abbreviated ECO, that enhances streaming-trajectory clustering quality by means of temporal smoothing that prevents abrupt changes in clusters across successive timestamps. Employing the notions of snapshot and historical trajectory costs, we formalize ECO and then formulate ECO as an optimization problem and prove that ECO can be performed approximately in linear time, thus eliminating the iterative processes employed in previous studies. Further, we propose a minimal-group structure and a seed point shifting strategy to facilitate temporal smoothing. Finally, we present all algorithms underlying ECO along with a set of optimization techniques. Extensive experiments with two real-life datasets offer insight into ECO and show that it outperforms state-of-the-art solutions in terms of both clustering quality and efficiency.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The widespread deployment of smartphones and location-enabled, networked in-vehicle devices renders it increasingly feasible to collect streaming trajectory data of moving objects. | background 1.00 | 0.06 | 0.01 | asserted 0.82 |
| s1 | The continuous clustering of such data can enable a variety of real-time services, such as identifying representative paths or common moving trends among objects in real-time. | background 0.99 | 0.06 | 0.03 | hedged possibility 0.94 |
| s2 | However, little attention has so far been given to the quality of clusters -- for example, it is beneficial to smooth short-term fluctuations in clusters to achieve robustness to exceptional data. | gap 0.99 | 0.06 | 0.03 | asserted 0.64 |
| s3 | We propose the notion of evolutionary clustering of streaming trajectories, abbreviated ECO, that enhances streaming-trajectory clustering quality by means of temporal smoothing that prevents abrupt changes in clusters across successive timestamps. | contribution 0.99 | 0.91 | 0.25 | asserted 1.00 |
| s4 | Employing the notions of snapshot and historical trajectory costs, we formalize ECO and then formulate ECO as an optimization problem and prove that ECO can be performed approximately in linear time, thus eliminating the iterative processes employed in previous studies. | method 0.76 | 0.95 | 0.11 | asserted 1.00 |
| s5 | Further, we propose a minimal-group structure and a seed point shifting strategy to facilitate temporal smoothing. | method 0.83 | 0.93 | 0.03 | asserted 0.99 |
| s6 | Finally, we present all algorithms underlying ECO along with a set of optimization techniques. | method 0.53 | 0.90 | 0.04 | asserted 0.88 |
| s7 | Extensive experiments with two real-life datasets offer insight into ECO and show that it outperforms state-of-the-art solutions in terms of both clustering quality and efficiency. | result 1.00 | 0.96 | 0.79 | asserted 0.99 |
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Original abstract
The widespread deployment of smartphones and location-enabled, networked in-vehicle devices renders it increasingly feasible to collect streaming trajectory data of moving objects. The continuous clustering of such data can enable a variety of real-time services, such as identifying representative paths or common moving trends among objects in real-time. However, little attention has so far been given to the quality of clusters -- for example, it is beneficial to smooth short-term fluctuations in clusters to achieve robustness to exceptional data. We propose the notion of evolutionary clustering of streaming trajectories, abbreviated ECO, that enhances streaming-trajectory clustering quality by means of temporal smoothing that prevents abrupt changes in clusters across successive timestamps. Employing the notions of snapshot and historical trajectory costs, we formalize ECO and then formulate ECO as an optimization problem and prove that ECO can be performed approximately in linear time, thus eliminating the iterative processes employed in previous studies. Further, we propose a minimal-group structure and a seed point shifting strategy to facilitate temporal smoothing. Finally, we present all algorithms underlying ECO along with a set of optimization techniques. Extensive experiments with two real-life datasets offer insight into ECO and show that it outperforms state-of-the-art solutions in terms of both clustering quality and efficiency.
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Cites as background 1 paper
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Comprehensive surveys of trajectory clustering are available [ref] .
A survey on trajectory clustering analysis · Streaming Trajectory Clustering · 1.00
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