Model-based Outlier Detection for Object-Relational Data

Outliers are anomalous and interesting objects that are notably different from the rest of the data. The outlier detection task has sometimes been considered as removing noise from the data. However, it is usually the significantly interesting deviations that are of most interest.Different outlier detection techniques work with various data formats. The outlier detection process needs to be sensitive to the nature of the underlying data. Most of the previous work on outlier detection was designed for propositional data. This dissertation focuses on developing outlier detection methods for structured data, more specifically object-relational data. Object-relational data can be viewed as a heterogeneous network with different classes of objects and links.We develop two new approaches to unsupervised outlier detection; both approaches leverage the statistical information obtained from a statistical-relational model. The first method develops a propositionalization approach to summarize information from object-relational data in a single data table.We use Markov Logic Network (MLN) structure learning to construct the features for the single data table and to mitigate the loss of information that usually happens when features are generated by manual aggregation. By using propositionalization as a pipeline, we can apply many previous outlier detection methods that were designed for single-table data.Our second outlier detection method ranks the objects as potential outliers in an object-oriented data model. Our key idea is to compare the feature distribution of a potential outlier object with the feature distribution of the object’s class. We introduce a novel distribution divergence concept that is suitable for outlier detection. Our methods are validated on synthetic datasets and on real-world datasets about soccer matches and movies.