Multi Relational Data Mining Classification Processions – A Survey

This paper commences with the introduction of multi relational data mining, which is an area widely explored nowadays because of its fruitfulness across a wide spectrum of applications. Data mining algorithms look for patterns in data. While most existing data mining approaches look for patterns in a single data table(propositionalisation), multi-relational data mining(MRDM) approaches look for patterns that involve multiple tables (relations) from a relational database. Three popular pattern finding techniques classification, clustering and association have been explored to a reasonable level of depth. Classification is an important task in data mining and machine learning, which has been studied extensively because of its usefulness. As converting data from multiple relations into single flat relation usually causes many problems, development of classification across multiple database relations becomes important. It then provides an insight into various classification methods including ILP(Inductive Logic Programming), Relational database, emerging patterns and associative approaches. Their characteristics and comparisons in detail have also been provided.