Research on Data Stream Amonaly Detection Using Incremental Clustering Ensemble
XU Fu
XU Jian
Abstract：Anomaly detection in data stream has gained a high attraction due to its applications,including real-time surveil-lance,network intrusion detection. However,traditional clustering is no longer suitable due to the particularity and timeliness of the data stream and the continuous characteristics of the data flow. Therefore,incremental clustering has become the research hotspots towards anomaly detection in data stream. An anomaly detection model in data stream is proposed based on two improved incremen-tal clustering aiming at the problem of low efficiency,high false positives and lack of pertinence of single clustering. The mothod is based on improved incremental clustering and an effective consensus function is designed to merge the results of a variety of cluster-ing algorithms. The experimental results show that the improved clustering algorithms are applicable to incremental clustering and they have better efficiency and better clustering result than single clustering method.
Keywords：data streamanomaly detectionincremental clusteringsubspace clusteringclustering ensemble
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 1473-1478,1508 )