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|Title:||Anomalies in multidimensional contexts||Contributor(s):||Dunstan, Neil (author); Despi, Ioan (author); Watson, Charles R (author)||Publication Date:||2009||Handle Link:||https://hdl.handle.net/1959.11/5484||Abstract:||This paper investigates the problem of presenting anomalies in a multidimensional data set. In such a data set, some dimensions may be merely descriptive, while others represent measures and attribute values used to determine whether the data is anomalous. A data cube of the descriptive dimensions is used as a data structure to partition the data set into sub-groups at each note, or context. It is shown that it is possible for a datum to be anomalous in more than one context. Previous work has dealt with this problem by embedding exception indicators in the data cube. Since the data cube is potentially large and anomalies are rare, searching for anomalies is inconvenient. Instead, it is proposed to construct a report for each anomaly that shows its status in each possible context. This results in a direct presentation of anomalous data.||Publication Type:||Conference Publication||Conference Name:||Data Mining 2009: the 10th International Conference on Data Mining, Detection, Protection and Security, Royal Mare Village, Crete, 27th - 29th May, 2009||Conference Details:||Data Mining 2009: the 10th International Conference on Data Mining, Detection, Protection and Security, Royal Mare Village, Crete, 27th - 29th May, 2009||Source of Publication:||Data Mining X: Data Mining, Detection and other Security Technologies - Proceedings of Data Mining 2009: the 10th International Conference on Data Mining, Detection, Protection and Security, p. 173-182||Publisher:||WIT Press||Place of Publication:||Southampton, England||ISSN:||1746-4463||Field of Research (FOR):||080109 Pattern Recognition and Data Mining||Socio-Economic Outcome Codes:||890399 Information Services not elsewhere classified||Peer Reviewed:||Yes||HERDC Category Description:||E1 Refereed Scholarly Conference Publication||Other Links:||http://www.wessex.ac.uk/09-conferences/data-mining-2009.html
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