Outlier Detection on Data Streams Using a QLattice-based Model and Online Learning

With the advancement of computer science, the dramatic developments in data mining area and their increasing applications, the identification of outlier or anomaly data has also become one of the most important research topics. In most applications, the outlier data contain beneficial information that can be used to gain useful knowledge. Today, there are a large number of applications on data streams, in the vast majority of which the discovery of outlier/anomaly data is very important and in some cases vital. Detection of anomalies is an important way for detecting frauds, network intrusion detection, detection of abnormal behaviors in monitoring systems, and other rare events that are always of great importance; but they are often difficult to identify. Most of the existing efficient outlier detection algorithms have been designed for the static data. While outlier detection is more challenging in data streams, where data are generating continuously and has especial properties such as infinity and transience. In this research, we introduce an approach based on the QLattice classification model, which works based on the quantum computing and performs better in the intended application than other classification methods. Given the possibility of changing the distribution of data over time in streaming data, a scheme to take advantage of online incremental learning is also applied in the proposed method. Considering the unlimited data flow and limited processing memory, the detection process is applied to a window of data that is constantly updated with data sampled from previous windows. A function is also designed to solve the problem of data imbalance, which uses the random sampling technique to solve this issue. The results of experiments obtained on benchmark datasets show that the proposed approach has better performance than other methods.