Concept acquisition approach of object-oriented concept lattices

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• 作者：Jian-Min Ma ; Ming-Jie Cai ; Cun-Jun Zou
• 关键词：Object ; oriented concept lattice ; Object ; oriented attribute concept ; Layered extension set ; Concept acquirement approach ; Concept acquirement algorithm
• 刊名：International Journal of Machine Learning and Cybernetics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：8
• 期：1
• 页码：123-134
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition;
• 出版者：Springer Berlin Heidelberg
• ISSN：1868-808X
• 卷排序：8

文摘

Formal concept analysis is an effective tool for data analysis and knowledge discovery. Corresponding to concept lattice in a formal context, object-oriented concept lattice is introduced based on rough set. Obtaining object-oriented concepts is important but difficult because of the higher time complexity. In order to solve this question, we first divide the power set of the attribute set into the layered sets in this paper. Since for any object-oriented concept, the object-oriented concept extension and object-oriented concept intension determine each other uniquely, we introduce the layered extension sets. By discussing the properties of layered extension sets, the approach to acquire object-oriented concepts is investigated, and related concept acquirement algorithm is also depicted. Examples prove that the concept acquirement approach is valid.