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
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