Yu, Jen ChienJen ChienYuYang, Chun ChiehChun ChiehYangGilbert, John ReubenJohn ReubenGilbertLiu, Rou JunRou JunLiuOyang, Yen-JenYen-JenOyangYang, Meng HanMeng HanYang2024-01-122024-01-122023-01-0197983503037802160133Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/638457A 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.Clustering analysis | Kernel density estimation | Spatiotemporal dataconference paper10.1109/ICMLC58545.2023.103279962-s2.0-85179847500https://api.elsevier.com/content/abstract/scopus_id/85179847500