ABSTRACT
The outlier detection problem in some cases is similar to the
classification problem. For example, the main concern of
clustering-based outlier detection algorithms is to find clusters
and outliers, which are often regarded as noise that should be
removed in order to make more reliable clustering.
In this article, we present an algorithm that provides outlier
detection and data clustering simultaneously. The
algorithmimprovesthe estimation of centroids of the
generative distribution during the process of clustering and
outlier discovery. The proposed algorithm consists of two
stages. The first stage consists of improved genetic k-means
algorithm (IGK) process, while the second stage iteratively
removes the vectors which are far from their cluster centroids.