Concept Compression in Formal Concept Analysis Using Entropy-Based Attribute Priority

References

• Assaghir, Z., M. Kaytoue, N. Messai, and A. Napoli. 2009. On the mining of numerical data with Formal Concept Analysis and similarity. Société Francophone de Classification 121–124.
   Google Scholar
• Babin, M. A., and S. O. Kuznetsov. 2012. Approximating concept stability. Proceedings of the International Conference on Formal Concept Analysis, May, 7–15. Berlin/Heidelberg: Springer, Leuven, Belgium.
   Google Scholar
• Belohlavek, R., and J. Macko. 2011. Selecting important concepts using weights. Proceedings of the International Conference on Formal Concept Analysis, May, 65–80. Berlin/Heidelberg: Springer, Nicosia, Cyprus.
   Google Scholar
• Belohlavek, R., and V. Vychodil. 2009. Formal concept analysis with background knowledge: Attribute priorities. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)39 (4):399–409. doi:10.1109/TSMCC.2008.2012168.
   PubMed Web of Science ®Google Scholar
• Carpineto, C., and G. Romano. 2004. Concept data analysis: Theory and applications. John Wiley & Sons, Chichester, England.
   Google Scholar
• Ch, A. K., S. M. Dias, and N. J. Vieira. 2015. Knowledge reduction in formal contexts using non-negative matrix factorization. Mathematics and Computers in Simulation 109:46–63. doi:10.1016/j.matcom.2014.08.004.
   Web of Science ®Google Scholar
• Cheung, S. K. 2004. Complexity reduction in lattice-based information retrieval: Theory, prototype development, and evaluation. Doctoral dissertation, City University of Hong Kong.
   Google Scholar
• Csiszar, I. 2008. Axiomatic characterizations of information measures. Entropy 10 (3):261–73. doi:10.3390/e10030261.
   Web of Science ®Google Scholar
• Davey, B. A., and H. A. Priestley. 2002. Introduction to lattices and order. Cambridge University Press, Cambridge, UK.
   Google Scholar
• Dias, S. M., and N. J. Vieira. 2013. Applying the JBOS reduction method for relevant knowledge extraction. Expert Systems with Applications 40 (5):1880–87. doi:10.1016/j.eswa.2012.10.010.
   Web of Science ®Google Scholar
• Fan, T. F., D. R. Liu, and G. H. Tzeng. 2007. Rough set-based logics for multicriteria decision analysis. European Journal of Operational Research 182 (1):340–55. doi:10.1016/j.ejor.2006.08.029.
   Web of Science ®Google Scholar
• Ganter, B., and R. Wille. 1989. Conceptual scaling. In Applications of combinatorics and graph theory to the biological and social sciences, 139–67. New York, US: Springer.
   Google Scholar
• Ganter, B., and R. Wille. 1999. Formal concept analysis: Mathematical foundations. (translated from the German by Cornelia Franzke). Ed., Fred S. Roberts, New York, US: Springer-Verlag.
   Google Scholar
• Hall, M. A., and G. Holmes. 2003. Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering, 15 (6):1437–47. doi:10.1109/TKDE.2003.1245283.
   Web of Science ®Google Scholar
• Han, J., and M. Kamber. 2006. Data mining, southeast asia edition: Concepts and techniques. New York, US: Morgan kaufmann.
   Google Scholar
• Kumar, C. 2011. Knowledge discovery in data using formal concept analysis and random projections. International Journal of Applied Mathematics and Computer Science 21 (4):745–56. doi:10.2478/v10006-011-0059-1.
   Web of Science ®Google Scholar
• Kumar, C. A. 2012. Fuzzy clustering-based formal concept analysis for association rules mining. Applied Artificial Intelligence 26 (3):274–301. doi:10.1080/08839514.2012.648457.
   Web of Science ®Google Scholar
• Kumar, C. A., M. S. Ishwarya, and C. K. Loo. 2015. Formal concept analysis approach to cognitive functionalities of bidirectional associative memory. Biologically Inspired Cognitive Architectures 12:20–33. doi:10.1016/j.bica.2015.04.003.
   Web of Science ®Google Scholar
• Kumar, C. A., and P. K. Singh. 2014. Knowledge representation using formal concept analysis: A study on concept generation. In Global trends in intelligent computing research and development, Eds., B. K. Tripathi, and D. P. Acharya, IGI Global Press, Hershey, Pennsylvania, USA, 11, 306-336. DOI: 10.4018/978-1-4666-4936-1
   Google Scholar
• Kumar, C. A., and S. Srinivas. 2010. Concept lattice reduction using fuzzy K-means clustering. Expert Systems with Applications 37 (3):2696–704. doi:10.1016/j.eswa.2009.09.026.
   Web of Science ®Google Scholar
• Kumar, C. A., and K. Sumangali. 2012. Performance evaluation of employees of an organization using formal concept analysis. Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME), Salem, India, 94–98. IEEE, March.
   Google Scholar
• Kuznetsov, S. O. 2001. On computing the size of a lattice and related decision problems. Order 18 (4):313–21. doi:10.1023/A:1013970520933.
   Web of Science ®Google Scholar
• Kuznetsov, S. O. 2007. On stability of a formal concept. Annals of Mathematics and Artificial Intelligence 49 (1–4):101–15. doi:10.1007/s10472-007-9053-6.
   Web of Science ®Google Scholar
• Kuznetsov, S. O., and S. A. Obiedkov. 2002. Comparing performance of algorithms for generating concept lattices. Journal of Experimental and Theoretical Artificial Intelligence 14 (2–3):189–216. doi:10.1080/09528130210164170.
   Web of Science ®Google Scholar
• Li, J., Z. He, and Q. Zhu. 2013. An entropy-based weighted concept lattice for merging multi-source geo-ontologies. Entropy 15 (6):2303–18. doi:10.3390/e15062303.
   Web of Science ®Google Scholar
• Li, J., C. A. Kumar, C. Mei, and X. Wang. 2017. Comparison of reduction in formal decision contexts. International Journal of Approximate Reasoning 80:100–22. doi:10.1016/j.ijar.2016.08.007.
   Web of Science ®Google Scholar
• Li, C., J. Li, and M. He. 2014. Concept lattice compression in incomplete contexts based on K-medoids clustering. International Journal of Machine Learning and Cybernetics 7 (4): 1–14. doi:10.1007/s13042-014-0288-3
   Web of Science ®Google Scholar
• Li, J., C. Mei, J. Wang, and X. Zhang. 2014. Rule-preserved object compression in formal decision contexts using concept lattices. Knowledge-Based Systems 71:435–45. doi:10.1016/j.knosys.2014.08.020.
   Web of Science ®Google Scholar
• Lv, Y. J., H. M. Liu, and J. H. Li. 2009. Attribute reduction of formal context based on concept lattice. Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery, vol. 1, Tianjin, China, 576–80. IEEE, August.
   Google Scholar
• Messai, N., M. D. Devignes, A. Napoli, and M. Smail-Tabbone. 2008. Many-valued concept lattices for conceptual clustering and information retrieval. ECAI, vol. 178, Patras, Greece 127–31, July.
   Google Scholar
• Oruç, O. E., E. Kuruoğlu, and O. Vupa. 2009. An application of entropy in survey scale. Entropy 11 (4):598–605. doi:10.3390/e11040598.
   Web of Science ®Google Scholar
• Priss, U. 2006. Formal concept analysis in information science. Arist 40 (1):521–43.
   Google Scholar
• Rice, M. D., and M. Siff. 2001. Clusters, concepts, and pseudometrics. Electronic Notes in Theoretical Computer Science 40:323–46. doi:10.1016/S1571-0661(05)80060-X.
   Google Scholar
• Rifkin, J. 1980. Entropy: A new world view. New York, US: Viking Press
   Google Scholar
• Rooker, J. C., and D. A. Gorard. 2007. Errors of intravenous fluid infusion rates in medical inpatients. Clinical Medicine 7 (5):482–85. doi:10.7861/clinmedicine.7-5-482.
   PubMed Web of Science ®Google Scholar
• Roth, C., S. Obiedkov, and D. Kourie. 2008. Towards concise representation for taxonomies of epistemic communities. In Concept lattices and their applications, Eds., R. Belohlavek, S. O. Kuznetsov, 240–55. Olomouc, Czech Republic, Europe, Berlin/Heidelberg:Springer.
   Google Scholar
• Shannon, C. E. 1948. A mathematical theory of communication, bell System technical Journal 27: 379–423 and 623–656. Mathematical Reviews (Mathscinet): MR10133e.
   Google Scholar
• Shivhare, R., and A. K. Cherukuri. 2017. Three-way conceptual approach for cognitive memory functionalities. International Journal of Machine Learning and Cybernetics 8(1), 21–34. doi:10.1007/s13042-016-0593-0.
   Web of Science ®Google Scholar
• Singh, P. K., C. Aswani Kumar, and A. Gani. 2016. A comprehensive survey on formal concept analysis, its research trends and applications. International Journal of Applied Mathematics and Computer Science 26 (2):495–516. doi:10.1515/amcs-2016-0035.
   Web of Science ®Google Scholar
• Singh, P. K., A. K. Cherukuri, and J. Li. 2015. Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy. International Journal of Machine Learning and Cybernetics 6(1): 1–11. doi: 10.1007/s13042-014-0313-6
   Web of Science ®Google Scholar
• Singh, P. K., and C. A. Kumar. 2016. Concept lattice reduction using different subset of attributes as information granules. In Granular computing, 1–15. doi:10.1007/s41066-016-0036-z
   Google Scholar
• Soldano, H., V. Ventos, M. Champesme, and D. Forge. 2010. Incremental construction of alpha lattices and association rules. Proceedings of the International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 351–60). Springer Berlin Heidelberg, Cardiff, UK, September, 351–360.
   Google Scholar
• Stumme, G. 2002. Efficient data mining based on formal concept analysis. Proceedings of the International Conference on Database and Expert Systems Applications, 534–46. Springer Berlin Heidelberg, Aix-en-Provence, France, September.
   Google Scholar
• Sumangali, K., and C. A. Kumar. 2013. Critical Analysis on Open Source LMSs using FCA. International Journal of Distance Education Technologies (IJDET)11 (4):97–111. doi:10.4018/IJDET.
   Google Scholar
• Sumangali, K., and C. Kumar. 2014. Determination of interesting rules in FCA using information gain. Proceedings of the 1st International Conference on Networks and Soft Computing (ICNSC), Guntur, India 304–08. IEEE, August.
   Google Scholar
• Tripathy, B. K., D. P. Acharjya, and V. Cynthya. 2013. A framework for intelligent medical diagnosis using rough set with formal concept analysis. International Journal of Artificial Intelligence and Applications 2(2): 45–66.
   Google Scholar
• Wille, R. 1982. Restructuring lattice theory: An approach based on hierarchies of concepts. In Ordered sets, 445–70. Netherlands: Springer.
   Google Scholar
• Zhang, S., P. Guo, J. Zhang, X. Wang, and W. Pedrycz. 2012. A completeness analysis of frequent weighted concept lattices and their algebraic properties. Data and Knowledge Engineering, Ed., I. Rival, Reidel, 81:104–17. doi:10.1016/j.datak.2012.08.002.
   Web of Science ®Google Scholar
• Zhang, S., and X. Wu. 2011. Fundamentals of association rules in data mining and knowledge discovery. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1 (2):97–116.
   Web of Science ®Google Scholar