A Fast Greedy Algorithm for Outlier Mining

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dc.creatorHe, Zengyou
dc.creatorXu, Xiaofei
dc.creatorDeng, Shengchun
dc.date2005-07-27
dc.date.accessioned2026-07-07T03:23:16Z
dc.date.available2026-07-07T03:23:16Z
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. In [38], the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our algorithm has comparable performance with respect to those state-of-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.
dc.description11 pages
dc.identifierhttps://arxiv.org/abs/cs/0507065
dc.identifierhttp://arxiv.org/abs/cs/0507065
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32875
dc.subjectDatabases
dc.subjectArtificial Intelligence
dc.titleA Fast Greedy Algorithm for Outlier Mining
dc.typetext

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