Outlier detection and classification in sensor data streams for proactive decision support systems

A paper has a deal with the problem of quality assessment in sensor data streams accumulated by proactive decision support systems. The new problem is stated where outliers need to be detected and to be classified according to their nature of origin. There are two types of outliers defined; the first type is about misoperations of a system and the second type is caused by changes in the observed system behavior due to inner and external influences. The proposed method is based on the data-driven forecast approach to predict the values in the incoming data stream at the expected time. This method includes the forecasting model and the clustering model. The forecasting model predicts a value in the incoming data stream at the expected time to find the deviation between a real observed value and a predicted one. The clustering method is used for taxonomic classification of outliers. Constructive neural networks models (CoNNS) and evolving connectionists systems (ECS) are used for prediction of sensors data. There are two real world tasks are used as case studies. The maximal values of accuracy are 0.992 and 0.974, and F1 scores are 0.967 and 0.938, respectively, for the first and the second tasks. The conclusion contains findings how to apply the proposed method in proactive decision support systems

Web Usage Mining in Tourism — A Query Term Analysis and Clustering Approach

According to current research, one of the most promising applications for web usage mining (WUM) is in identifying homogenous user subgroups (Liu, 2008). This paper presents a prototypical workflow and tools for analyzing user sessions to extract business intelligence hidden in web log data. By considering a leading Swedish destination gateway, we demonstrate how query term analysis in combination with session clustering can be utilized to effectively explore the information needs of website users. The system thus overcomes many of the limitations of typical web site analysis tools that only offer general statistics and ignore the opportunities offered by unsupervised learning techniques

Measuring robustness for weighted distributions: Bayesian perspective

Bayesian Robustness, ε-contamination, Weighted distribution,