Dynamic Graph-Based Clustering for Non-Stationary Spatio-Temporal Event Prediction
Cascading spatial temporal pattern mining is the process of getting event as a partial order set of getting space and time in one order pair. The order pairs are disjoint and unique with location constraint. In this article the crime data set and represented the ordered pairs as nodes. The event tha...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/16/e3sconf_icregcsd2025_02006.pdf |
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| Summary: | Cascading spatial temporal pattern mining is the process of getting event as a partial order set of getting space and time in one order pair. The order pairs are disjoint and unique with location constraint. In this article the crime data set and represented the ordered pairs as nodes. The event that occurred next is taken as edge from one node to another node. Graph terminology as homogeneous and heterogeneous with kinds of problems are solved. Representation of Graph gives us the crime data analysis with location wise and helps us to predict the next occurrence instance. An alternate way of modeling the objects in data sets is to represent those using graphs. Frequent pattern discovery of events and occurrence is by sub graphs from entire data sets. Experiment with data events points and occurrence evaluation of the performance of a pattern using data sets. |
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| ISSN: | 2267-1242 |