CS 420 Chapter Notes - Chapter 2: Association Rule Learning, Pattern Matching, Tree Traversal
14 Jun 2019
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Document Summary
The values of nominal (or categorical) attributes are. Names of things. nominal attributes have a finite number of possible values, with no ordering among the values (e. g. , occupation, brand, color). Quantitative attributes are numeric and have an implicit ordering among values (e. g. , age, income, price). Techniques for mining multidimensional association rules can be categorized into two basic approaches regarding the treatment of quantitative attributes. In the first approach, quantitative attributes are discretized using predefined concept hierarchies. For instance, a concept hierarchy for income may be used to replace the original numeric values of this attribute by interval labels such as 020k, 21k30k, 31k40k, and so on. These intervals can then be treated as nominal attributes (where each interval is considered a category). We refer to this as mining multidimensional association rules using static discretization of quantitative attributes. In the second approach, quantitative attributes are discretized or clustered into bins based on the data distribution.
