Mining relational databases with multi-view learning

Most of today's structured data resides in relational databases where multiple relations are formed by foreign key joins. In recent years, the field of data mining has played a key role in helping humans analyze and explore large databases. Unfortunately, most methods only utilize "flat" data representations. Thus, to apply these single-table data mining techniques, we are forced to incur a computational penalty by first converting the data into this "flat" form. As a result of this transformation, the data not only loses its compact representation but the semantic information present in the relations are reduced or eliminated. In this paper, we describe a classification approach, which addresses this issue by operating directly on relational databases. The approach, called MVC (Multi-View Classification), is based on a multi-view learning framework. In this framework, the target concept is represented in different views and then independently learned using single-table data mining techniques. After constructing multiple classifiers for the target concept in each view, the learners are validated and combined by a meta-learning algorithm. Two methods are employed in the MVC approach, namely (1) target concept propagation and (2) multi-view learning. The propagation method constructs training sets directly from relational databases for use by the multi-view learners. The learning method employs traditional single-table mining techniques to mine data straight from a multi-relational database. Our experiments on benchmark real-world databases show that the MVC method achieves promising results in terms of overall accuracy obtained and run time, when compared with the FOIL and CrossMine learning methods.