Efficient Outlier Detection Algorithm for Heterogeneous Data Streams

Data streams outlier mining is an important and active research issue in anomaly detection. Most of the existing outlier detection algorithms can only manipulate numeric attributes or categorical attributes. In this paper, we propose an efficient outlier detection algorithm based on heterogeneous data streams, which partitions the stream in chunks. Then each chunk is clustered and the corresponding clustering results are stored in cluster references. The representation degree and the number of adjacent cluster references of each cluster reference are computed to generate the final outlier references, which include potential outliers. Experimental results show that our approach has higher detection precision and better scalability.