An efficient multi relational framework using fuzzy rule-based classification technique

Multi relational classification is one of the rapidly rising subclasses of multi relational data mining. It focuses on identifying attractive patterns in an interconnected multiple database relations via primary key/foreign key relationship. Many conventional rule-based classification approaches produce the rules from single 'flat' relation and typically suffer from rule clashes. However, many real world data repositories are stored in multiple database relations. Owing to the increase in the quantity of relational data that are being accumulated and the limitation of propositional rule-based classifiers in relational domains, multi relational classification has turned out to be a field with significance. In this work, a multi relational fuzzy rule system (MRFRS) is proposed for classification across multiple database relations based on PART algorithm that generates crisp rules. These rules are used to formulate the multi relational fuzzy rule system that produces rules of mutually exclusive and exhaustive in nature. The empirical results show that the proposed method outperforms well on real datasets in terms of efficiency and accuracy.