Mining targeted spatio-temporal sequential patterns.
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| Title: | Mining targeted spatio-temporal sequential patterns. |
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| Authors: | Maciąg, Piotr S.1 (AUTHOR) piotr.maciag@pw.edu.pl |
| Source: | GeoInformatica. Jul2025, Vol. 29 Issue 3, p435-463. 29p. |
| Subjects: | Sequential pattern mining, Spatiotemporal processes, Algorithms, Mathematical optimization, Data analysis |
| Abstract: | This article addresses the problem of Target-oriented Spatio-temporal Sequential Pattern (TaSTSP) mining by introducing the concept of TaSTSP and its theoretical properties. It presents the TaSTSPM algorithm, a tool designed for efficient and effective pattern mining, incorporating four pruning strategies to accelerate the mining process. The article examines the time and space complexity of the proposed algorithms. Experiments using the Boston Crime Dataset and the Seattle Collision Dataset demonstrate that TaSTSPM outperforms other state-of-the-art algorithms in identifying spatio-temporal sequential patterns. Finally, the article provides examples of useful patterns discovered from these datasets. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This article addresses the problem of Target-oriented Spatio-temporal Sequential Pattern (TaSTSP) mining by introducing the concept of TaSTSP and its theoretical properties. It presents the TaSTSPM algorithm, a tool designed for efficient and effective pattern mining, incorporating four pruning strategies to accelerate the mining process. The article examines the time and space complexity of the proposed algorithms. Experiments using the Boston Crime Dataset and the Seattle Collision Dataset demonstrate that TaSTSPM outperforms other state-of-the-art algorithms in identifying spatio-temporal sequential patterns. Finally, the article provides examples of useful patterns discovered from these datasets. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13846175 |
| DOI: | 10.1007/s10707-025-00535-1 |
