Outlier detection in data streams has gained wide importance presently due to
the increasing cases of fraud in various applications of data streams. The
techniques for outlier detection have been divided into either statistics
based, distance based, density based or deviation based. Till now, most of the
work in the field of fraud detection was distance based but it is incompetent
from computational point of view. In this paper we introduced a new clustering
based approach, which divides the stream in chunks and clusters each chunk
using kmedian into variable number of clusters.