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.