Automated quality control of tropical cyclone winds through data mining

H. Nicholas Carrasco, Mei Ling Shyu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The analysis of tropical cyclones (TC) depends heavily on the quality of the incoming data set. With the advances in technology, the sizes of these data sets also increase. There is a great demand for an efficient and effective unsupervised quality control tool. Towards such a demand, data mining algorithms like spatial clustering and specialized distance measures can be applied to perform this task. This paper reports our findings on the studies on utilizing a density-based clustering algorithm with three different distance measures on a series of TC data sets.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005
Pages229-234
Number of pages6
DOIs
StatePublished - Dec 1 2005
Event2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005 - Las Vegas, NV, United States
Duration: Aug 15 2005Aug 17 2005

Publication series

NameProceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005
Volume2005

Other

Other2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005
CountryUnited States
CityLas Vegas, NV
Period8/15/058/17/05

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Carrasco, H. N., & Shyu, M. L. (2005). Automated quality control of tropical cyclone winds through data mining. In Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005 (pp. 229-234). [1506478] (Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005; Vol. 2005). https://doi.org/10.1109/IRI-05.2005.1506478