文件名称:Link-basedOutlierandAnomalyDetectioninEvolvingData
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In this paper, we present LOADED, an algorithm for outlier
detection in evolving data sets containing both continuous
and categorical attributes. LOADED is a tunable algorithm,
wherein one can trade off computation for accuracy so that
domain-specific response times are achieved. Experimental
results show that LOADED provides very good detection and
false positive rates, which are several times better than those
of existing distance-based schemes.
detection in evolving data sets containing both continuous
and categorical attributes. LOADED is a tunable algorithm,
wherein one can trade off computation for accuracy so that
domain-specific response times are achieved. Experimental
results show that LOADED provides very good detection and
false positive rates, which are several times better than those
of existing distance-based schemes.
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