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Classification Approaches on Relational Databases


T.Hemalatha, J.Samatha, Ch.Vani Priyanka, A.Lavanya, Ch.Ranjith Kumar


Vol. 13  No. 4  pp. 60-65


Relational databases are the most popular repository for structured data, and are thus one of the richest sources of knowledge in the world. In a relational database, multiple relations are linked together via entity-relationship links. Classification is an important task in data mining and machine learning, which has been studied extensively because of its usefulness development of classification across multiple database relations, becomes important. Multi relational classification is the procedure of building a classifier based on information stored in multiple relations and making predictions with it. There are many popular approaches for finding patterns in data. This paper 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.


Tuple id propagation, Crossmine, Classification