| Issue |
E3S Web Conf.
Volume 616, 2025
2nd International Conference on Renewable Energy, Green Computing and Sustainable Development (ICREGCSD 2025)
|
|
|---|---|---|
| Article Number | 02006 | |
| Number of page(s) | 8 | |
| Section | Green Computing | |
| DOI | https://doi.org/10.1051/e3sconf/202561602006 | |
| Published online | 24 February 2025 | |
Dynamic Graph-Based Clustering for Non-Stationary Spatio-Temporal Event Prediction
1 Research Scholar, SRM Institute of Science & Technology, Kattankulanthur, India
2 Professor, SRM Institute of Science Technology, Kattankulanthur, India
3 Professor, CVR College of Engineering, Hyderabad, India
* Corresponding author: vm4200@srmist.edu.in
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.
© The Authors, published by EDP Sciences, 2025
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