https://scholars.lib.ntu.edu.tw/handle/123456789/638457
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yu, Jen Chien | en_US |
dc.contributor.author | Yang, Chun Chieh | en_US |
dc.contributor.author | Gilbert, John Reuben | en_US |
dc.contributor.author | Liu, Rou Jun | en_US |
dc.contributor.author | Oyang, Yen-Jen | en_US |
dc.contributor.author | Yang, Meng Han | en_US |
dc.date.accessioned | 2024-01-12T08:50:49Z | - |
dc.date.available | 2024-01-12T08:50:49Z | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.isbn | 9798350303780 | - |
dc.identifier.issn | 2160133X | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/638457 | - |
dc.description.abstract | 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. | en_US |
dc.relation.ispartof | Proceedings - International Conference on Machine Learning and Cybernetics | en_US |
dc.subject | Clustering analysis | Kernel density estimation | Spatiotemporal data | en_US |
dc.title | Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator | en_US |
dc.type | conference paper | en_US |
dc.identifier.doi | 10.1109/ICMLC58545.2023.10327996 | - |
dc.identifier.scopus | 2-s2.0-85179847500 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85179847500 | - |
dc.relation.pageend | 422 | en_US |
item.openairetype | conference paper | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.fulltext | no fulltext | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | Biomedical Electronics and Bioinformatics | - |
crisitem.author.dept | Computer Science and Information Engineering | - |
crisitem.author.dept | Networking and Multimedia | - |
crisitem.author.dept | Center for Systems Biology | - |
crisitem.author.dept | Genome and Systems Biology Degree Program | - |
crisitem.author.orcid | 0000-0002-4286-0637 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | College of Life Science | - |
Appears in Collections: | 生醫電子與資訊學研究所 |
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