The results of anomaly detection are sensitive to the choice of detection algorithms as they are specialized for different properties of data, especially for multidimensional data. Thus, it is vital to select the algorithm appropriately. To systematically select the algorithms, ensemble analysis techniques have been developed to support the assembly and comparison of heterogeneous algorithms. However, challenges remain due to the absence of the ground truth, interpretation, or evaluation of these anomaly detectors. In this paper, we present a visual analytics system named EnsembleLens that evaluates anomaly detection algorithms based on the ensemble analysis process. The system visualizes the ensemble processes and results by a set of novel visual designs and multiple coordinated contextual views to meet the requirements of correlation analysis, assessment and reasoning of anomaly detection algorithms. We also introduce an interactive analysis workflow that dynamically produces contextualized and interpretable data summaries that allow further refinements of exploration results based on user feedback. We demonstrate the effectiveness of EnsembleLens through a quantitative evaluation, three case studies with real-world data and interviews with two domain experts.

In this paper, we apply Fuzzy Data Mining and Forecasting to the process of the fuzzy data in the field of oil exploration. Based on the advantages and disadvantages of the traditional Kriging method and the Multi-point Geostatistics in geostatistic, the paper proposes the KM method, a new simulation estimation method, to reflect the geological features of a region by combing the traditional Kriging method with Multi-point Geostatistics. In the following paper, we introduce several key issues about the new approach and then put forward corresponding solutions for them, and finally, demonstrate the rationality and validity of the method by comparing the simulation test results.