Representative association rules and minimum condition maximum consequence association rules
AbstractDiscovering association rules (AR) among items in a large database is an important database mining problem. The number of association rules may be large. To alleviate this problem, we introduced in  a notion of representative association rules (RR. RR is a least set of rules that covers all association rules. The association rules, which are not representative ones, may be generated by means of a cover operator without accessing a database. On the other hand, a subset of association rules that allows to predict as much as possible from minimum facts is usually of interest to analysts. This kind of rules we will call minimum condition maximum consequence rules (MMR). In this paper, we investigate the relationship between RR and MMR. We prove that MMR is a subset of RR and it may be extracted from RR.
|Book||Zytkow Jan, Quafafou Mohamed (eds.): Principles of Data Mining and Knowledge Discovery, Lecture Notes In Computer Science, no. 1510, 1998, Springer Berlin Heidelberg, ISBN 978-3-540-65068-3, 978-3-540-49687-8|
|Keywords in English||Artificial Intelligence (incl. Robotics), Business Information Systems, Information Storage and Retrieval, Information Systems Applications (incl.Internet)|
|Citation count*||57 (2018-01-25)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.