Discretization Numerical Data for Relational Data with One-to-Many Relations
Rayner Alfred · Journal of Computer Science · 2009
Abstract: Problem statement: Handling numerical data stored in a relational database has been performed differently from handling those numerical data stored in a single table due to the multiple occurrences (one-to-many association) of an individual record in the non-target table and non-determinate relations between tables. Numbers in Multi-Relational Data Mining (MRDM) were often discretized, after considering the schema of the relational database. Study the effects of taking the one-to-many association issue into consideration in the process of discretizing continuous numbers. Approach: Different alternatives for dealing with continuous attributes in MRDM were considered in this study, namely equal-width (EWD), Equal-Height (EH), equal-weight (EWG) and Entropy-Based (EB). The discretization procedures considered in this study included algorithms that were not depended on the multi-relational structure of the data and also that are sensitive to this structure. A new method of discretization, called the entropy instance-based (EIB) discretization method was implemented and evaluated with respect to C4.5 on the two well-known multi-relational databases that include the Mutagenesis dataset and the Hepatitis dataset for Discovery Challenge PKDD 2005. Results: When the number of bins, b, is big (b = 8), the entropy-instance-based discretization method produced better data summarization results compared to the other discretization methods, in the mutagenesis dataset. In