Abstract
Classification is an important task in data mining and machine learning, which has been studied extensively and has a wide range of applications. Lots of algorithms have been proposed to build accurate and scalable classifiers. Most of these algorithms can only applied to single “flat“ relations, whereas in the real world most data are stored in multiple tables. As converting data from multiple relations into single flat relation usually causes many problems, development of classification across multiple database relations becomes important. In this paper, we present the several kinds of classification method across multiple database relations including Inductive Logic Programming (ILP), Relational database, Emerging Pattern, Associative approaches and their characteristics, the comparisons in detail.
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