Incremental Local Outlier Detection for Data Streams

Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further complicated by the fact that in many cases, outliers have to be detected from data streams that arrive at an enormous pace. In this paper, an incremental LOF (Local Outlier Factor) algorithm, appropriate for detecting outliers in data streams, is proposed. The proposed incremental LOF algorithm provides equivalent detection performance as the iterated static LOF algorithm (applied after insertion of each data record), while requiring significantly less computational time. In addition, the incremental LOF algorithm also dynamically updates the profiles of data points. This is a very important property, since data profiles may change over time. The paper provides theoretical evidence that insertion of a new data point as well as deletion of an old data point influence only limited number of their closest neighbors and thus the number of updates per such insert...

• Type: Conference
• Year: 2007
• Where: CIDM
• Authors: Dragoljub Pokrajac, Aleksandar Lazarevic, Longin Jan Latecki
• Keywords: Artificial Intelligence, CIDM 2007, Data Stream, Incremental Lof Algorithm, Outliers