An Optimization Model for Outlier Detection in Categorical Data

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dc.creatorHe, Zengyou
dc.creatorXu, Xiaofei
dc.creatorDeng, Shengchun
dc.date2005-03-29
dc.date.accessioned2026-07-07T03:22:47Z
dc.date.available2026-07-07T03:22:47Z
dc.descriptionThe task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Detection of such outliers is important for many applications such as fraud detection and customer migration. Most existing methods are designed for numeric data. They will encounter problems with real-life applications that contain categorical data. In this paper, we formally define the problem of outlier detection in categorical data as an optimization problem from a global viewpoint. Moreover, we present a local-search heuristic based algorithm for efficiently finding feasible solutions. Experimental results on real datasets and large synthetic datasets demonstrate the superiority of our model and algorithm.
dc.description12 pages
dc.identifierhttps://arxiv.org/abs/cs/0503081
dc.identifierhttp://arxiv.org/abs/cs/0503081
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32685
dc.subjectDatabases
dc.subjectArtificial Intelligence
dc.titleAn Optimization Model for Outlier Detection in Categorical Data
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