On Mining Rules that Involve Inequalities from Decision Table
| Document type: | Conference Papers |
|---|---|
| Peer reviewed: | Yes |
| Author(s): | Yang Liu, Guohua Bai, Boqin Feng |
| Title: | On Mining Rules that Involve Inequalities from Decision Table |
| Conference name: | Cognitive Informatics, 2008. ICCI 2008. 7th IEEE |
| Year: | 2008 |
| Pagination: | 255 - 260 |
| ISBN: | 978-1-4244-2538-9 |
| Publisher: | IEEE CS Press |
| City: | Stanford University, CA, USA |
| URI/DOI: | 10.1109/COGINF.2008.4639176 |
| Organization: | Blekinge Institute of Technology |
| Department: | School of Engineering - Dept. of Interaction and System Design (Sektionen för teknik – adv. för interaktion och systemdesign) School of Engineering S- 372 25 Ronneby +46 455 38 50 00 http://www.tek.bth.se/ |
| Authors e-mail: | yli@bth.se |
| Language: | English |
| Abstract: | We introduce the notion of generating decision rules that involve inequalities. While a conventional decision rule expresses the trivial equality relations between attributes and values from the same or different objects, inequality rules express the non-equivalent relationships between attributes and values. The problem of mining inequality rules is formulated as a process of mining equality rules from a compensatory decision table. In order to mine high-order inequality rules, one can transform the original decision table to a high-order compensatory decision table, in which each new entity is a pair of objects. Any standard data-mining algorithm can then be used. We theoretically analyze the complexity of proposed models based on their meta-level representation in cognitive informatics. Mining inequalities in decision table makes a complementary feature of a rule induction system, which may result in generating a small number of short rules for domains where attributes have large number of values, and when majority of them are correlated with the same decision class. |
| Subject: | Computer Science\Artificial Intelligence Computer Science\Computersystems Computer Science\General |
| Keywords: | Rough set, decision table, rule induction, inequality rules, knowledge representation |
