| Abstract |
The discovery of patterns by rule induction algorithms like Subgroup Discovery, usually results
in a large number of patterns. The patterns discovered tend to capture the essence of only a part of
the dataset the algorithm was applied to. Unfortunately, the patterns are not mutually exclusive and,
therefore contain a lot of redundancy. In order to capture the essence of the dataset with a small set
of patterns, global models can be constructed from the patterns, so-called pattern teams. Pattern
Team Discovery uses quality measures to qualify candidate pattern teams. An example of such a quality
measure is the accuracy of a classifier learned on a candidate pattern team. The research in this thesis
focuses on the performance of linear classifiers used to obtain the quality of a candidate pattern team.
The second goal consists of whether a linear classifier performs better on a pattern team than non-linear
classifiers. This is achieved by comparing the Decision Table Majority classifier with the Support Vector
Machine classifier. |
