Abstract: Outlier detection in data streams poses great challenges due to the limited memory availability and real time detection rectuirement. A fast outlier detection algorithm in mixed data streams was introduced by clustering the data streams incrementally based on the damped model and generating the cluster features on behalf of the data distribution.The radius threshold value changed dynamically. When detection requirement was received the outlier factor of specified clusters was calculated and the clusters with high outlier factor were taken as the abnormal clusters. At the same time the method is proposed to distinguish between the abnormal cluster and the initial stage of data evolution. The complexity of the time and space were nearly linear with the size of data streams. The experimental results on the KDDCUP99 dataset demonstrate that the method can effectively detect the outliers in mixed data streams.

Key words: Mixed attribute, Data streams, Incremental clustering, Outlier detection, Damped model