RODD: Robust Outlier Detection in Data Cubes
This addresses outlier detection for data analysts working with multidimensional databases, though it appears incremental as it builds on existing outlier detection methods.
The authors tackled the problem of outlier detection in multidimensional data cubes, which had not been extensively studied, by introducing RODD-RF, a novel random forest-based approach, and comparing it with traditional methods. Results from simulations and real-world data showed that RODD-RF led to improved outlier detection.
Data cubes are multidimensional databases, often built from several separate databases, that serve as flexible basis for data analysis. Surprisingly, outlier detection on data cubes has not yet been treated extensively. In this work, we provide the first framework to evaluate robust outlier detection methods in data cubes (RODD). We introduce a novel random forest-based outlier detection approach (RODD-RF) and compare it with more traditional methods based on robust location estimators. We propose a general type of test data and examine all methods in a simulation study. Moreover, we apply ROOD-RF to real world data. The results show that RODD-RF can lead to improved outlier detection.