Spatio-temporal outlier detection in large databases

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Birant D., Kut R. A.

Journal of Computing and Information Technology, vol.14, no.4, pp.291-297, 2006 (Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 4
  • Publication Date: 2006
  • Doi Number: 10.2498/cit.2006.04.04
  • Journal Name: Journal of Computing and Information Technology
  • Journal Indexes: Scopus, Applied Science & Technology Source, Central & Eastern European Academic Source (CEEAS), Compendex, Computer & Applied Sciences, INSPEC, Library and Information Science Abstracts, Directory of Open Access Journals, Library, Information Science & Technology Abstracts (LISTA)
  • Page Numbers: pp.291-297
  • Keywords: Data warehouse, Datamining, Outlier detection, Spatio-temporal data
  • Dokuz Eylül University Affiliated: Yes


Outlier detection is one of the major data mining methods. This paper proposes a three-step approach to detect spatio-temporal outliers in large databases. These steps are clustering, checking spatial neighbors, and checking temporal neighbors. In this paper, we introduce a new outlier detection algorithm to find small groups of data objects that are exceptional when compared with the remaining large amount of data. In contrast to the existing outlier detection algorithms, the new algorithm has the ability of discovering outliers according to the non-spatial, spatial and temporal values of the objects. In order to demonstrate the new algorithm, this paper also presents an example of application using a data warehouse.