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Outlier detection in data streams has gained wide importance presently due tothe increasing cases of fraud in various applications of data streams. Thetechniques for outlier detection have been divided into either statisticsbased, distance based, density based or deviation based. Till now, most of thework in the field of fraud detection was distance based but it is incompetentfrom computational point of view. In this paper we introduced a new clusteringbased approach, which divides the stream in chunks and clusters each chunkusing kmedian into variable number of clusters. Instead of storing completedata stream chunk in memory, we replace it with the weighted medians foundafter mining a data stream chunk and pass that information along with the newlyarrived data chunk to the next phase. The weighted medians found in each phaseare tested for outlierness and after a given number of phases, it is eitherdeclared as a real outlier or an inlier. Our technique is theoretically betterthan the k-means as it does not fix the number of clusters to k rather gives arange to it and provides a more stable and better solution which runs inpoly-logarithmic space.

A Cluster-based Approach for Outlier Detection in Dynamic Data Streams (KORM: k-median OutlieR Miner)