An Incremental Approach for Updating Approximations Based on Set-Valued Ordered Information Systems

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• 作者：Chuan Luo (1) luochuan@my.swjtu.edu.cn
  Tianrui Li (1) trli@swjtu.edu.cn
  Hongmei Chen (1) hmchen@swjtu.edu.cn
  Dun Liu (2) newton83@163.com
• 关键词：Rough set theory – ; approximations – ; incremental learning – ; information systems
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7413
• 期：1
• 页码：363-369
• 全文大小：157.8 KB
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• 作者单位：1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031 China2. School of Economics and Management, Southwest Jiaotong University, Chengdu, 610031 China
• ISSN：1611-3349

文摘

Incremental learning is an efficient technique for knowledge discovery in a dynamic database. Rough set theory is an important mathematical tool for data mining and knowledge discovery in information systems. The lower and upper approximations in the rough set theory may change while data in the information system evolves with time. In this paper, we focus on the incremental updating principle for computing approximations in set-valued ordered information systems. The approaches for updating approximations are proposed when the object set varies over time.