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On detecting spatial categorical outliers / X. Liu in Geoinformatica, vol 18 n° 3 (July 2014)  

Public [article]  inGeoinformatica > vol 18 n° 3 (July 2014) . - pp 501 - 536 Titre : On detecting spatial categorical outliers Type de document : Article/Communication Auteurs : X. Liu, Auteur ; Feng Chen, Auteur ; Tien Lu, Auteur Année de publication : 2014 Article en page(s) : pp 501 - 536 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] détection d'anomalie [Termes IGN] valeur aberrante Résumé : (Auteur)Spatial outlier detection is an important research problem that has received much attentions in recent years. Most existing approaches are designed for numerical attributes, but are not applicable to categorical ones (e.g., binary, ordinal, and nominal) that are popular in many applications. The main challenges are the modeling of spatial categorical dependency as well as the computational efficiency. This paper presents the first outlier detection framework for spatial categorical data. Specifically, a new metric, named as Pair Correlation Ratio (PCR), is measured for each pair of category sets based on their co-occurrence frequencies at specific spatial distance ranges. The relevances among spatial objects are then calculated using PCR values with regard to their spatial distances. The outlierness for each object is defined as the inverse of the average relevance between an object and its spatial neighbors. Those objects with the highest outlier scores are returned as spatial categorical outliers. A set of algorithms are further designed for single-attribute and multi-attribute spatial categorical datasets. Extensive experimental evaluations on both simulated and real datasets demonstrated the effectiveness and efficiency of our proposed approaches. Numéro de notice : A2014-499 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-013-0188-9 Date de publication en ligne : 28/09/2013 En ligne : https://doi.org/10.1007/s10707-013-0188-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74091 [article]

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