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New Technology of Library and Information Service  2010, Vol. 26 Issue (10): 17-22    DOI: 10.11925/infotech.1003-3513.2010.10.03
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Comparative Study on ConExp and Lattice Miner
Teng Guangqing, Bi Qiang
School of Management,Jilin University, Changchun 130022,China
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This paper firstly builds concept lattice of some ball-games with ConExp1.3 and Lattice Miner1.4. Then it compares the quality and operation of the two tools from the basic information, modification of formal context, layout of lattice, mining of association rules and storage management. ConExp stresses the concept and the relationships of concepts, and personalized presentation of the concept lattice; and Lattice Miner has advantages to deal with the complex problem, extract association rules, and support semantic network. It makes the foundation for the research based on concept lattice tool.

Key wordsConExp      Lattice      Miner      Formal      context      Concept      lattice      Association      rule     
Received: 06 September 2010      Published: 04 January 2011



Cite this article:

Teng Guangqing, Bi Qiang. Comparative Study on ConExp and Lattice Miner. New Technology of Library and Information Service, 2010, 26(10): 17-22.

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[1] Wille R. Restructuring Lattice Theory: An Approach Based on Hierarchies of Concept . In: Proceedings of the 7th International Conference, ICFCA 2009. LNCS 5548. Berlin: Springer-Verlag, 2009: 314-339.

[2] Overview on ConExp . .

[3] Becker P, Correia H J. The ToscanaJ Suite for Implementing Conceptual Information Systems . In: Proceedings of Formal Concept Analysis 2005. LNCS 3626. Berlin: Springer-Verlag, 2005: 324–348.

[4] Lattice Miner 1.4 Beta . .

[5] Szathmary L, Napoli A. CORON: A Framework for Levelwise Itemset Mining Algorithms . .

[6] Concept Explorer . .

[7] Lattice Miner . .

[8] Wormuth B, Becker P. Introduction to Formal Concept Analysis . . http://www.

[9] Bělohlávek Radim. Introduction to Formal Concept Analysis . . esf/ucebni/formal.pdf.

[10] Wille R. Methods of Conceptual Knowledge Processing . In: Proceedings of the 4th International Conference, ICFCA 2006. LNCS 3874. Berlin: Springer-Verlag, 2006: 1-29.

[11] Ganter B, Wille R. Applied Lattice Theory: Formal Concept Analysis . .

[12] Concept Explorer:The User Guide . .

[13] Stumme G. Efficient Data Mining Based on Formal Concept Analysis . In: Proceedings of the 13th International Conference on Database and Expert Systems Applications. LNCS 2453. London: Springer-Verlag, 2002: 534-546.

[14] Burmeister P. Formal Concept Analysis with ConImp: Introduction to the Basic Features . .

[15] Galicia’s Features . .

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