BibliographicReference 107223

BibliographicReference record

Type: Bibliographic resource
Type of document: Book chapters
Type of document: Conference paper
BibLvlCode: AMS
Title: Identifying erroneous data using outlier detection techniques
Abstract: Common data quality problems observed in OBIS are described. BSCAN, a density-based clustering algorithm for large spatial data bases is employed to identify geographical outliers in federated data from a public Web service on the OBIS Portal. The algorithm is shown to be effective and efficient for this purpose. The relationship between outliers and erroneous data points are discussed and the future plan to develop an operational data quality checking tool based on this algorithm is discussed.
Bibliographic citation: Zhuang, W.; Zhang, Y.; Grassle, J.F. (2007). Identifying erroneous data using outlier detection techniques, in: Vanden Berghe, E. et al. (Ed.) Proceedings Ocean Biodiversity Informatics: International Conference on Marine Biodiversity Data Management, Hamburg, Germany 29 November to 1 December, 2004. VLIZ Special Publication, 37: pp. 187-192
Topic: Marine
Access rights: open access
Is accessible for free: true

term: Clustering (term code: 62987 - defined in term set: CSA Technology Research Database Master Thesaurus); Clustering (term code: 111141 - defined in term set: CAB Thesaurus); Data (term code: 2086 - defined in term set: ASFA Thesaurus List); Quality assurance (term code: 6649 - defined in term set: ASFA Thesaurus List); Quality control (term code: 6650 - defined in term set: ASFA Thesaurus List)