Comprehensive and Evolution Study Focusing Future Research Challenges in the Field of Multi Relational Data Mining Specific to Multi-Relational Classification Approaches

Abstract

Most of today’s structured data is stored in relational databases. Thus, the task of learning from relational data has begun to receive significant attention in the literature. 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. As an important task of multi- relational data mining, multi-relational classification can directly look for patterns that involve multiple relations from a relational database and have more advantages than propositional data mining approaches. According to the differences in knowledge representation and strategy, the paper addressed different kind of multi-relational classification approaches that are ILP- based, graph-based and relational database-based classification approaches and discussed each relational classification technology, their characteristics, the comparisons and several challenging researching problems in detail.