Formal concept analysis and concept lattice: Perspectives and challenges

被引：7
作者：
Yan, Hehua [1 ]
Zou, Caifeng [2 ]
Liu, Jianqi [2 ]
Wang, Zhonghai [3 ]
机构：
[1] College of Electrical Engineering, Guangdong Mechanical and Electrical College, Guangzhou
[2] College of Information Engineering, Guangdong Mechanical and Electrical College, Guangzhou
[3] College of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou
来源：
International Journal of Autonomous and Adaptive Communications Systems | 2015年 / 8卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Concept lattice; Data mining; FCA; Formal concept analysis; Lattice construction algorithm;
D O I：
10.1504/IJAACS.2015.067710
中图分类号：
学科分类号：
摘要：
Formal concept analysis (FCA) is a powerful tool for data mining, ontology research, web semantic retrieval, software engineering, and knowledge discovery. Concept lattice is the core data structure of FCA. Association rules mining methods based on concept lattices are discussed. The algorithms of constructing concept lattices are introduced, and the merits and drawbacks of these algorithms are compared. The research situation about attribute reduction of concept lattice is given. Furthermore, the extended models of concept lattice and the challenges to development of concept lattice are introduced. At last, many problems on FCA and concept lattice needed to study deeply are given. Copyright © 2015 Inderscience Enterprises Ltd.
引用
收藏
页码：81 / 96
页数：15
相关论文
共 73 条
[1]  
Al-Ekram R., Kontogiannis K., Source code modularization using lattice of concept slices, Proceedings of the 8th European Conference on Software Maintenance and Reengineering, CSMR 2004, pp. 195-203, (2004)
[2]  
Bordat J.P., Practical calculation of lattice Galois correspondence, Mathematiques et Sciences Humaines, 96, 1, pp. 31-47, (1986)
[3]  
Bruggemann R., Patil G.P., Formal Concept Analysis, Ranking and Prioritization for Multi-Indicator Systems, pp. 117-133, (2011)
[4]  
Burusco A., The study of the l-fuzzy concept lattice, Mathware & Soft Computing, 1, 3, pp. 209-218, (2008)
[5]  
Cai Y., Cercone N., Han J., An attribute-oriented approach for learning classification rules from relational databases, Proceedings of Sixth International Conference on Data Engineering, pp. 281-288, (1990)
[6]  
Carpineto C., Romano G., Information retrieval through hybrid navigation of lattice representations, International Journal of Human-Computer Studies, 45, 5, pp. 553-578, (1996)
[7]  
Chein M., Algorithme de recherche des sous-matrices premieres d'une matrice, Bull. Math. Soc. Sci. Math. R.S. Roumanie, 1, 13, pp. 21-25, (1969)
[8]  
Chen M., Wan J., Gonzalez S., Liao X., Leung V.C.M., A survey of recent developments in home M2M networks, IEEE Communications Surveys & Tutorials, 16, 1, pp. 98-114, (2014)
[9]  
Cheung K.K., Vogel D., Complexity reduction in lattice-based information retrieval, Information Retrieval, 8, 2, pp. 285-299, (2005)
[10]  
Cimiano P., Hotho A., Stumme G., Tane J., Conceptual knowledge processing with formal concept analysis and ontologies, Proc. Of the Second Intl. Conf. On Formal Concept Analysis (ICFCA 04), pp. 189-207, (2004)
12345678