Swipe to navigate through the chapters of this book
This chapter looks at the question of how to convert a continuous attribute to a categorical one, a process known as discretisation. This is important as many data mining algorithms, including TDIDT, require all attributes to take categorical values.
Two different types of discretisation are distinguished, known as local and global discretisation. The process of extending the TDIDT algorithm by adding local discretisation of continuous attributes is illustrated in detail, followed by a description of the ChiMerge algorithm for global discretisation. The effectiveness of the two methods is compared for the TDIDT algorithm for a number of datasets.
Please log in to get access to your license.
Dont have a licence yet? Then find out more about our products and how to get one now:
go back to reference Kerber, R. (1992). ChiMerge: discretization of numeric attributes. In Proceedings of the 10th national conference on artificial intelligence (pp. 123–128). Menlo Park: AAAI Press. Kerber, R. (1992). ChiMerge: discretization of numeric attributes. In Proceedings of the 10th national conference on artificial intelligence (pp. 123–128). Menlo Park: AAAI Press.
- Continuous Attributes
Prof. Max Bramer
- Copyright Year
- Springer London