Detecting Projected Outliers in High-Dimensional Data Streams

Abstract. In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector (SPOT), to identify outliers embedded in subspaces. Sparse Subspace Template (SST), a set of subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. MultiObjective Genetic Algorithm (MOGA) is employed as an effective search method for finding outlying subspaces from training data to construct SST. SST is able to carry out online self-evolution in the detection stage to cope with dynamics of data streams. The experimental results demonstrate the efficiency and effectiveness of SPOT in detecting outliers in high-dimensional data streams.

• Type: Conference
• Year: 2009
• Where: DEXA
• Authors: Ji Zhang, Qigang Gao, Hai H. Wang, Qing Liu, Kai Xu 0003
• Keywords: Data Streams, Database, DEXA 2009, Dimensional Data Streams, Outliers