Multi-granularity Classification Rule Discovery Using ERID
This paper introduces the use of ERID 1 algorithm for classification rule discovery at various levels of granularity.We use an incomplete information system and attribute value hierarchy to extract rules.The incomplete information system is capable of storing weighted attribute values and the domains of those attributes are organized using a hierarchical tree structure.The granularity of attribute values can be adjusted using the attribute value hierarchy.The result is then processed through ERID,which is designed to discover rules from partially incomplete information systems.The capability of handling incomplete data enables to build more specific and general classification rules.
knowledge discovery incomplete information system at-tribute hierarchy rough sets
Seunghyun Im Zbigniew W.Ra(s) Li-Shiang Tsay
Department of Computer Science University of Pittsburgh at Johnstown Johnstown,PA 15904,USA Department of Computer Science University of North Carolina Charlotte,NC 28223,USA Department of Electronics,Computer and Information Technology North Carolina A&T University Greensbo
国际会议
成都
英文
491-499
2008-05-17（万方平台首次上网日期，不代表论文的发表时间）