  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. Instead of storing complete
data stream chunk in memory, we replace it with the weighted medians found
after mining a data stream chunk and pass that information along with the newly
arrived data chunk to the next phase. The weighted medians found in each phase
are tested for outlierness and after a given number of phases, it is either
declared as a real outlier or an inlier. Our technique is theoretically better
than the k-means as it does not fix the number of clusters to k rather gives a
range to it and provides a more stable and better solution which runs in
poly-logarithmic space.

Distributed, Parallel, and Cluster Computing