Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in stat...

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Bibliographic Details
Main Authors: Yu, Jen-Chien, Yang, Chun-Chieh, Gilbert, John Reuben, Liu, Rou-Jun, Oyang, Yen-Jen, Yang, Meng-Han
Format: Conference Proceeding
Language:English
Subjects:
Clustering analysis
Estimation
Kernel density estimation
Machine learning
Medical services
Probability density function
Sociology
Spatiotemporal data
Spatiotemporal phenomena
Urban areas
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Summary:A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset.
ISSN:2160-1348
DOI:10.1109/ICMLC58545.2023.10327996