Outlier Mining Study Over Data Streams

Outlier detection is an important data mining task. Recently, online discovering outlier under data streams model has attracted attention for many emerging applications, such as network intrusion detection. Because the algorithms on data streams are restricted to fulfill their works with only one pass over data sets and limited resources, it is a very challenging problem to detect outliers over streams.Real-time surveillance systems, telecommunication systems, and other dynamic environments often generate tremendous (potentially infinite) volume of stream data: the volume is too huge to be scanned multiple times. Much of such data resides at rather low level of abstraction, whereas most analysts are interested in relatively high-level dynamic changes (such as trends and outliers). To discover such high-level characteristics, one may need to perform on-line multi-level, multi-dimensional analytical processing of stream data.Based on outlier mining algorithms and data streams analysis, the thesis proposes an algorithm of outlier mining in data streams. This algorithm uses discovery-driven exploration of OLAP data cubes method. First, we propose an architecture called stream cube, to facilitate on-line, multi-dimensional, multi-level analysis of stream data. Second, an outlier mining method based on constrained-cube is demonstrated. At last, the thesis apply the stream data outlier mining method to smart phone intrusion detection, we promote a simple model of smart phone intrusion detection.