Earlier research has resulted in the production of an ‘all-rules’ algorithm for data-mining that produces all conjunctive rules of above given confidence and coverage thresholds. While this is a useful tool, it may produce a large number of rules. This paper describes the application of two clustering algorithms to these rules, in order to identify sets of similar rules and to better understand the data.
|Title of host publication||Intelligent Data Engineering and Automated Learning – IDEAL 2004|
|Editors||Zheng Rong Yang, Hujun Yin, Richard M. Everson|
|Number of pages||6|
|Publication status||Published - 2004|
|Name||Lecture Notes in Computer Science|