The Hybrid Approach for Handling and Detecting Outliers from Dynamic Data Stream

The Outlier detection is currently area of active research in data set mining community. In this article we propose hybrid approach to capture outliers in dynamic data stream. We apply k-mean algorithm which Partition the data set into number of chunks or clusters. Each chunk contains set of data. Once cluster are formed, centroid of each cluster are calculated. The points which are lying near the centroid of the cluster are not probable candidate outlier and we can prune out such points from each cluster. Next distance based technique is used to find the distance from centroid to candidate outlier. For that threshold value is set. If this distance is greater than threshold value then it will declare as outlier otherwise as a real object. In proposed approach, two techniques are combining to efficiently find the outlier from the data set. This hybrid approach takes less computational cost. Proposed algorithm efficiently prune of the safe cells and save huge number of extra calculations.