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Nowadays many data mining algorithms focus on clustering methods. There are also a lot of approaches designed for outlier detection. We observe that, in many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Thus, it is necessary to treat clusters and outliers as concepts of the same importance in data analysis. In this paper, we present a cluster-outlier iterative detection algorithm, tending to detect thedoi:10.1109/icde.2005.146 dblp:conf/icde/ShiZ05 fatcat:gqzonqm7z5fb7a6gmmom3vig7e