Asian Journal of Information Technology

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STEM: A Novel Approach for Spatiotemporal Sequence Mining

Kelvin Leong and Stephen Chan
Page: 94-99 | Received 21 Sep 2022, Published online: 21 Sep 2022

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Abstract

Building on the skeleton of Generalized Sequential Pattern (GSP), researchers propose a new approach-Spatio-Temporal Events Miner (STEM) for sequential pattern analysis. The STEM extends the traditional finding by coverage of both temporal and spatial attributes. Furthermore, researchers are interested in studying whether AprioriAll Method in traditional GSP is most suitable in STEM.

How to cite this article:

Kelvin Leong and Stephen Chan. STEM: A Novel Approach for Spatiotemporal Sequence Mining.
DOI: https://doi.org/10.36478/ajit.2012.94.99
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2012.94.99