Authors
Amol Ghoting, Matthew Eric Otey, Srinivasan Parthasarathy
Publication date
2004/11/1
Conference
Fourth IEEE International Conference on Data Mining (ICDM'04)
Pages
387-390
Publisher
IEEE
Description
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.
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Scholar articles
A Ghoting, ME Otey, S Parthasarathy - Fourth IEEE International Conference on Data Mining …, 2004