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2016 | 26 | 2 | 495-516
Tytuł artykułu

A comprehensive survey on formal concept analysis, its research trends and applications

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In recent years, FCA has received significant attention from research communities of various fields. Further, the theory of FCA is being extended into different frontiers and augmented with other knowledge representation frameworks. In this backdrop, this paper aims to provide an understanding of the necessary mathematical background for each extension of FCA like FCA with granular computing, a fuzzy setting, interval-valued, possibility theory, triadic, factor concepts and handling incomplete data. Subsequently, the paper illustrates emerging trends for each extension with applications. To this end, we summarize more than 350 recent (published after 2011) research papers indexed in Google Scholar, IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and a few authoritative fundamental papers.
Rocznik
Tom
26
Numer
2
Strony
495-516
Opis fizyczny
Daty
wydano
2016
otrzymano
2014-04-25
poprawiono
2014-12-11
poprawiono
2015-04-10
zaakceptowano
2015-05-26
Twórcy
  • Centre for Mobile Cloud Computing Research, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
  • School of Information Technology and Engineering, VIT University, Vellore 632014, India
  • Centre for Mobile Cloud Computing Research, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Bibliografia
  • Akram, M. and Dudek, W.A. (2011). Interval valued fuzzy graphs, Computers Mathematics with Applications 61(2): 289-299.
  • Alcalde, C., Burusco, A., Fuentes-González, R. and Zubia, I. (2011). The use of linguistic variables and fuzzy propositions in the L-fuzzy concept theory, Computers and Mathematics with Applications 62(8): 3111-3122.
  • Alcalde, C., Burusco, A. and Fuentes-González, R. (2012a). Analysis of certain L-fuzzy relational equations and the study of its solutions by means of the L-fuzzy concept theory, International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems 20(1): 21-40.
  • Alcalde, C., Burusco, A. and Fuentes-González, R. (2012b). Composition of L-fuzzy contexts, Proceedings of the 10th ICFCA 2012, Leuven, Belgium, pp. 1-14 .
  • Alcalde, C., Burusco, A. and Fuentes-González, R. (2012c). The study of fuzzy context sequences, International Journal of Computational Intelligence Systems 6(3): 518-529.
  • Alcalde, C., Burusco, A. and Fuentes-González, R. (2015). The use of two relations in L-fuzzy contexts, Information Sciences 301: 1-14.
  • Alqadah, F. and Bhatnagar, R. (2012). Similarity measures in formal concept analysis, Annals of Mathematics and Artificial Intelligence 61(3): 245-256.
  • Amin, I.I., Kassim, S.K., Hassanien, A.E. and Hefny, H.A. (2012). Formal concept analysis for mining hypermethylated genes in breast cancer tumor subtypes, Proceedings of 12th ISDA, 2012, Kochi, India, pp. 764-769.
  • Annapurna, J. and Aswani Kumar, Ch. (2013). Exploring attribute with domain knowledge in formal concept analysis, Journal of Computing and Information Technology 21(2): 109-123.
  • Antoni, L., Krajci, S., Kridlo, O., Macek, B. and Piskova, L. (2014). On heterogeneous formal contexts, Fuzzy Sets and Systems 234: 22-33.
  • Aswani Kumar, Ch. (2011a). Reducing data dimensionality using random projections and fuzzy K-means clustering, International Journal of Intelligent Computing and Cybernetics 4(3): 353-365.
  • Aswani Kumar, Ch. (2011b). Knowledge discovery in data using formal concept analysis and random projections, International Journal of Applied Mathematics and Computer Science 21(4): 745-756, DOI: 10.2478/v10006-011-0059-1.
  • Aswani Kumar, Ch. (2012). Fuzzy clustering-based formal concept analysis for association rules mining, Applied Artificial Intelligence 26(3): 274-301.
  • Aswani Kumar, Ch. (2013). Designing role-based access control using formal concept analysis, Security and Communication Networks 6(3): 373-383.
  • Aswani Kumar, Ch., Radvansky, M., Fuentes-Gonzlez, R. and Annapurna, J. (2012). Analysis of a vector space model, latent semantic indexing and formal concept analysis for information retrieval, Cybernetics and Information Technologies 12(1): 34-48.
  • Aswani Kumar, Ch., Dias, S.M. and Vieira, N.J. (2015a). Knowledge reduction in formal contexts using non-negative matrix factorization, Mathematics and Computers in Simulation 109: 46-63.
  • Aswani Kumar, Ch., Ishwaryaa, M.S. and Loo, C.K. (2015b). Formal concept analysis approach to cognitive functionalities of bidirectional associative memory, Biologically Inspired Cognitive Architectures 22: 20-33, DOI:10.1016/j.bica.2015.04.003.
  • Aswani Kumar, Ch. and Singh, P.K. (2014). Knowledge representation using formal concept analysis: A study on concept generation, in B.K. Tripathy and D.P. Acharjya (Eds.), Global Trends in Knowledge Representation and Computational Intelligence, IGI Global Publishers, Hershey, PA, pp. 306-336.
  • Aswani Kumar, Ch. and Srinivas, S. (2010). Concept lattice reduction using fuzzy K-means clustering, Expert Systems with Application 37(3): 2696-2704.
  • Atif, J., Hudelot, C. and Bloch, I. (2014). Explanatory reasoning for image understanding using formal concept analysis and description logics, IEEE Transactions on Systems, Man, and Cybernetics A 44(4): 552-570.
  • Aufaure, M.A. and Grand, B.L. (2013). Advances in FCA-based applications for social networks analysis, International Journal of Conceptual Structures and Smart Applications 1(1): 73-89.
  • Babin, M.A. and Kuznetsov, S.O. (2012). Approximating concept stability, in F. Domenach et al. (Eds.), Proceedings of the 10th International Conference, ICFCA 2012, Lecture Notes in Computer Science, Vol. 7278, Springer, Berlin/Heidelberg, pp. 7-15.
  • Babin, M.A. and Kuznetsov, S.O. (2013). Computing premises of minimal cover of functional dependencies is intractable, Discrete Applied Mathematics 161(6): 742-749.
  • Bartl, E., Rezankova, H. and Sobisek, L. (2011). Comparison of classical dimensionality reduction methods with novel approach based on formal concept analysis, in J.T. Yao et al. (Eds.), Rough Set and Knowledge Technology, Lecture Notes in Computer Science, Vol. 6954, Springer, Berlin/Heidelberg, pp. 26-35.
  • Bazhanov, K. and Obiedkov, S. (2014). Optimizations in computing the Duquenne-Guigues basis of implications, Annals of Mathematics and Artificial Intelligence 70(1): 5-24.
  • Belohlavek, R. (2012). Optimal decompositions of matrices with entries from residuated lattices, Annals of Mathematics and Artificial Intelligence 22(6): 1405-1425.
  • Belohlavek, R., Baets, B.D. and Konecny, J. (2014). Granularity of attributes in formal concept analysis, Information Sciences 260(5): 149-170.
  • Belohlavek, R., Glodeanu, C. and Vychodil, V. (2013a). Optimal factorization of three-way binary data using triadic concepts, Order 30(2): 437-454.
  • Belohlavek, R., Kostak, M. and Osicka, P. (2013b). Formal concept analysis with background knowledge: A case study in paleobiological taxonomy of belemnites, International Journal of General Systems 42(4): 426-440.
  • Belohlavek, R., and Macko, J. (2011). Selecting important concepts using weights, in P. Valtchev et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 6628, Springer, Berlin/Heidelberg, pp. 65-80.
  • Belohlavek, R., Sigmund, E. and Zacpal, J. (2011a). Evaluation of IPAQ questionnaires supported by formal concept analysis, Information Sciences 181(10): 1774-1786.
  • Belohlavek, R., Osicka, P. and Vychodil, V. (2011b). Factorizing three-way ordinal data using triadic formal concepts, in H. Christiansen et al. (Eds.), Flexible Query Answering Systems, Lecture Notes in Computer Science, Vol. 7022, Springer, Berlin/Heidelberg, pp. 400-411.
  • Belohlavek, R., and Osicka, P. (2012a). Triadic fuzzy Galois connections as ordinary connections, IEEE International Conference on Fuzzy Systems, Brisbane, Australia, pp. 1-6.
  • Belohlavek, R. and Osicka, P. (2012b). Triadic concept lattices of data with graded attributes, International Journal of General Systems 41(2): 93-108.
  • Belohlavek, R., and Trnecka, M. (2013). Basic level in formal concept analysis: Interesting concepts and psychological ramifications, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, pp. 1233-1239.
  • Belohlavek, R. and Vychodil, V. (2005). Fuzzy attribute logic: Entailment and non-redundant basis, 11th International Fuzzy Systems Association World Congress, Tsinghua, China, pp. 622-627.
  • Belohlavek, R. and Vychodil, V. (2012). Formal concept analysis and linguistic hedges, International Journal of General Systems 41(5): 503-532.
  • Biao, X., Ruairi, de F., Eric, R. and Micheal, F. (2012). Distributed formal concept analysis algorithms based on an iterative map reduce framework, in F. Domenach et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 7278, Springer, Berlin/Heidelberg, pp. 292-308.
  • Bloch, I. (2011). Lattices of fuzzy sets and bipolar fuzzy sets and mathematical morphology, Information Sciences 181(10): 2002-2015.
  • Borgwardt, S. and Penaloza, R. (2014). Consistency reasoning in lattice-based fuzzy description logics, International Journal of Approximate Reasoning 55(9): 1917-1938.
  • Bouaud, J., Messai, N., Laouenan, C., Mentre, F. and Seroussi, B. (2013). Elicitating patient patterns of physician non-compliance with breast cancer guidelines using formal concept analysis, Studies in Health Technology and Informatics 180: 471-481.
  • Butka, P., Pocs, J. and Pocsova, J. (2012). Use of concept lattices for data tables with different types of attributes, Journal of Information and Organizational Sciences 36(1): 1-12.
  • Chen, J., Lia, J., Lin, Y., Lin, G. and Ma, Z. (2015). Relations of reduction between covering generalized rough sets and concept lattices, Information Sciences 304: 16-27.
  • Chen, R.C., Bau, C.T. and Yeh, C.J. (2011). Merging domain ontologies based on the WordNet system and fuzzy formal concept analysis techniques, Applied Soft Computing 11(2): 1908-1923.
  • Ciobanu, G. and Vaideanu, C. (2014). Similarity relations in fuzzy attribute-oriented concept lattices, Fuzzy Sets and Systems 275: 88-109.
  • Codocedo, V., Taramasco, C. and Astudillo, H. (2011). Cheating to achieve formal concept analysis over a large formal context, Proceedings of the 11th International Conference on Concept Lattices and Their Applications, Kosice, Slovakia, pp. 349-362.
  • Codocedo, V., Lykourentzou, I. and Napoli, A. (2012). Semantic querying of data guided by formal concept analysis, Formal Concept Analysis for Artificial Intelligence, Nancy, France.
  • Cook, T.M. and Coombs, M. (2004). Using formal concept analysis (FCA) to model and represent counterdeception analytic tasks, Proceedings of the 13th International Conference on Behavior Representation in Modeling and Simulation, Arlington, VA, USA, pp. 7-8.
  • Croitorua, M., Orenb, N., Milesc, S. and Luckc, M. (2012). Graphical norms via conceptual graphs, Knowledge-Based Systems 29: 31-43.
  • Dau, F. (2013). Towards scalingless generation of formal contexts from an ontology in a triple stores, International Journal of Conceptual Structures and Smart Applications 1(1): 18-38.
  • Davey, B.A. and Priestley, H.A. (2002). Introduction to Lattices and Order, Cambridge University Press, Cambridge.
  • De Maio, C., Fenza, G., Loia, V. and Senatore, S. (2012a). Hierarchical web resources retrieval by exploiting fuzzy formal concept analysis, Information Processing and Management 48(3): 399-418.
  • De Maio, C., Fenza, G., Gaeta, M., Loia, V., Orciuoli, F. and Senatore, S. (2012b). RSS-based e-learning recommendations exploiting fuzzy FCA for knowledge modeling, Applied Soft Computing 12(1): 113-124.
  • De Maio, C., Fenza, G., Gallo, M., Loia, V. and Senatore, S. (2014). Formal and relational concept analysis for fuzzy-based automatic semantic annotation, Applied Intelligence 40(1): 153-174.
  • Denniston, J.T., Melton, A. and Rodabaugh, S.E. (2013). Formal concept analysis and lattice-valued Chu systems, Fuzzy Sets and Systems 216: 52-90.
  • Dias, S.M., Zarate, L.E. and Vieira, N.J. (2013). Extracting reducible knowledge from ANN with JBOS and FCANN approaches, Expert Systems with Applications 40(8): 3087-3095.
  • Dias, S.M., and Vieira, N.J. (2013). Applying the JBOS reduction method for relevant knowledge extraction, Expert Systems with Applications 40(5): 1880-1887.
  • Dias, S.M. and Vieira, N.J. (2015). Concept lattices reduction: Definition, analysis and classification, Expert Systems with Applications 42(20): 7084-7097, DOI: 10.1016/j.eswa.2015.04.044
  • Distel, F. (2012). Adapting fuzzy formal concept analysis for fuzzy description logics, Proceedings of CLA, Fuengirola, Spain, pp. 163-174.
  • Djouadi, Y. (2011). Extended Galois derivation operators for information retrieval based on fuzzy formal concept lattice, in S. Benferhat et al. (Eds.), Scalable Uncertainty Management, Lecture Notes in Computer Science, Vol. 6929, Springer, Berlin/Heidelberg, pp. 346-358.
  • Djouadi, Y. and Prade, H. (2009). Interval-valued fuzzy formal concept analysis, in J. Rauch et al. (Eds.), Foundations of Intelligent System, Lecture Notes in Artificial Intelligence, Vol. 5722, Springer, Berlin/Heidelberg, pp. 592-601.
  • Doerfel, S., Jaschke, R. and Stumme, G. (2012). Publication analysis of the formal concept analysis community, in F. Domenach et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 7278, Springer, Berlin/Heidelberg, pp. 77-95.
  • Du, Y. and Hai, Y. (2013). Semantic ranking of web pages based on formal concept analysis, Journal of Systems and Software 86(1): 187-197.
  • Dubois, D. and Prade, H. (2012). Possibility theory and formal concept analysis: Characterizing independent sub-contexts, Fuzzy Sets and Systems 196: 4-16.
  • Endres D., Adam, R., Giese. M.A. and Noppeney, U. (2012). Understanding the semantic structure of human fMRI brain recordings with formal concept analysis, in F. Domenach et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 7278, Springer, Berlin/Heidelberg, pp. 96-111.
  • Eklund, P., Ducrou, J. and Dau, F. (2012). Concept similarity and related categories in information retrieval using formal concept analysis, International Journal of General Systems 41(8): 826-846.
  • Elzinga, P., Viaene, S., Poelmans, J., Dedene, G. and Morsing, S. (2010). Terrorist threat assessment with formal concept analysis, Proceedings of the 2010 IEEE International Conference on Intelligence and Security Informatics, Vancouver, BC, Canada, pp. 77-82.
  • Fan, F., Hong, W., Song, J., Jing, J. and Ji, S. (2013). A visualization method for Chinese medicine knowledge discovery based on formal concept analysis, ICIC Express Letters 4(3): 801-808.
  • Ferjani, F., Elloumi, S., Jaoua, A., Ben Yahia, S., Ismail, S. and Ravan, S. (2012). Formal context coverage based on isolated labels: An efficient solution for text feature extraction, Information Sciences 188: 198-214.
  • Formica, A. (2012). Semantic web search based on rough sets and fuzzy formal concept analysis, Knowledge-Based Systems 26(3): 40-47.
  • Formica, A. (2013). Similarity reasoning for the semantic web based on fuzzy concept lattices: An informal approach, Information Systems Frontiers 15(3): 511-520.
  • Fowler, M. (2013). The taxonomy of a Japanese stroll garden: An ontological investigation using formal concept analysis, Axiomathes 13(1): 43-59.
  • Fu, H. and Mephu Nguifo, E. (2004). A parallel algorithm to generate formal concepts for large data, in P. Eklund (Ed.), Concept Lattices, Lecture Notes in Computer Science, Vol. 2961, Springer, Berlin/Heidelberg, pp. 394-401.
  • Ganter, B. and Glodeanu, C.V. (2012). Ordinal factor analysis, in F. Domenach et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 7278, Springer, Berlin/Heidelberg, pp. 128-139.
  • Ganter, B. and Meschke, C. (2011). A formal concept analysis approach to rough data tables, in H. Sakai et al. (Eds.), Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Lecture Notes in Computer Science, Vol. 6600, Springer, Berlin/Heidelberg, pp. 37-61.
  • Ganter, B. and Wille, R. (1999). Formal Concept Analysis: Mathematical Foundation, Springer-Verlag, Berlin.
  • Galitsky, B.A., Ilvovsky, D., Strok, F. and Kuznetsov, S.O. (2013). Improving text retrieval efficiency with pattern structures on parse thickets, Proceedings of FCAIR 2013, Moscow, Russia, pp. 6-21.
  • Glodeanu, C.V. (2011). Factorization with hierarchical classes analysis and formal concept analysis, in P. Valtchev et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 6628, Springer, Berlin/Heidelberg, pp. 107-118.
  • Glodeanu, C.V. (2012). Attribute dependency in fuzzy setting, Proceedings of CLA 2012, Fuengirola, Spain, pp. 127-138.
  • Glodeanu, C.V. and Ganter, B. (2012). Applications of ordinal factor analysis, in P. Cellier et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 7880, Springer, Berlin/Heidelberg pp. 109-124.
  • Gonzalez Calabozo, J.M., Pelaez-Moreno, C. and Valverde-Albacete, F.J. (2011). Gene expression array exploration using K-formal concept analysis, in P. Valtchev and R. Jäschke (Eds.), Proceedings of the 9th International Conference ICFCA 2011, Lecture Notes in Computer Science, Vol. 6628, Springer, Berlin/Heidelberg, pp. 119-134.
  • Hamrouni, T., Ben Yahia, S. and Mephu Nguifo, E. (2013). Looking for a structural characterization of the sparseness measure of (frequent closed) itemset contexts, Information Sciences 222: 343-361.
  • Helen, Z., David, J. and Zhao, X.J. (2013). Construction of new energy-saving building materials based on formal concept analysis methods, Advanced Materials Research 738: 133-136.
  • Helmi, B.H., Rahmani, A.T. and Pelikan, M. (2014). A factor graph based genetic algorithm, International Journal of Applied Mathematics and Computer Science 24(3): 621-633, DOI: 10.2478/amcs-2014-0045.
  • Ignatov, D.I., Kuznetsov, S.O., Magizov, R.A. and Zhukov, L.E. (2011). From triconcepts to triclusters, in S.O. Kuznetsov et al. (Eds.) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Lecture Notes in Computer Science, Vol. 6743, Springer, Berlin/Heidelberg, pp. 257-264.
  • Ignatov, D.I., Gnatyshak, D.V., Kuznetsov, S.O. and Mirkin, B.G. (2015). Triadic formal concept analysis and triclustering: Searching for optimal patterns, Machine Learning 101(1): 271-302, DOI:10.1007/s10994-015-5487-y.
  • Ilvovsky, D. and Klimushkin, M. (2013). FCA-based search for duplicate objects in ontologies, Proceedings of FCAIR, Moscow, Russia, pp. 36-46.
  • Iordache, O. (2011). Modeling multi-level systems, Understanding Complex Systems 70: 143-163.
  • Junli, L., Zongyi, H. and Qiaoli, Z. (2013). An entropy-based weighted concept lattice for merging multi-source geo-ontologies, Entropy 15(6): 2303-2318.
  • Kaiser, T.B. and Schmidt, S.E. (2013). A macroscopic approach to FCA and its various fuzzifications, in F. Domenach et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 7278, Springer, Berlin/Heidelberg, pp. 140-147.
  • Kang, X., Li, D., Wang, S. and Qu, K. (2012a). Formal concept analysis based on fuzzy granularity base for different granulations, Fuzzy Sets and Systems 203: 33-48.
  • Kang, X., Li, D., Wang, S. and Qu, K. (2012b). Rough set model based on formal concept analysis, Information Sciences 222: 611-625.
  • Kaytoue, M., Kuznetsov, S.O., Napoli, A. and Polaillon, G. (2011a). Symbolic data analysis and formal concept analysis, XVIIIeme Rencontres de la Societe Francophone de Classification-SFC, Orléans, France, pp. 1-4.
  • Kaytoue, M., Kuznetsov, S.O., Napoli, A. and Duplessis, S. (2011b). Mining gene expression data with pattern structures in formal concept analysis, Information Sciences 181: 1989-2001.
  • Krajca, P, Outrata, J. and Vychodil, V. (2008). Parallel recursive algorithm for FCA, Proceedings of CLA, Olomouc, Czech Republic, pp. 71-82.
  • Krajca, P., Outrata, J. and Vychodil, V. (2012). Concept lattices of incomplete data, in F. Domenach et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 7278, Springer, Berlin/Heidelberg, pp. 180-194.
  • Korei, A. (2013). Applying formal concept analysis in machine-part grouping problems, Proceedings of the 11th International Symposium on Applied Machine Intelligence and Informatics 2013, Herl'any, Slovakia, pp. 197-200.
  • Kuznetsov, S.O. (2013). Fitting pattern structures to knowledge discovery in big data, in P. Cellier et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 7880, Springer, Berlin/Heidelberg, pp. 254-266.
  • Kuznetsov, S.O. and Obiedkov, S.A. (2002). Comparing performance of algorithms for generating concept lattices, Journal of Experimental and Theoretical Artificial Intelligence 14(2-3): 189-216.
  • Kuznetsov, S.O. and Poelmans, J. (2013). Knowledge representation and processing with formal concept analysis, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3(3): 200-215.
  • Langdon, W.B., Yoo, S. and Harma, M. (2011). Formal concept analysis on graphics hardware, Proceedings of CLA, Nancy, France, pp. 413-416.
  • Lei, Y. and Tian, J. (2012). Concepts with negative-values and corresponding concept lattices, Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery, Sichuan, China, pp. 1005-1008.
  • Li, J., Changlin, M. and Yuejin, L. (2011a). A heuristic knowledge-reduction method for decision formal contexts, Computers and Mathematics with Applications 61(4): 1096-1106.
  • Li, J., Changlin, M. and Yuejin, L. (2011b). Knowledge reduction in decision formal contexts, Knowledge-Based Systems 24(5): 709-715.
  • Li, J., Mei, C. and Lv, Y. (2012a). Knowledge reduction in real decision formal contexts, Information Sciences 189(5): 191-207.
  • Li, J., Mei, C. and Lv, Y. (2012b). Knowledge reduction in formal decision contexts based on an order-preserving mapping, International Journal of General Systems 41(5): 143-161.
  • Li, J., Mei, C. and Lv, Y. (2013a). Incomplete decision contexts: Approximate concept construction, rule acquisition and knowledge reduction, International Journal of Approximate Reasoning 54(1): 149-165.
  • Li, J., Mei, C., Aswani Kumar, Ch. and Lv, Y. (2013b). On rule acquisition in decision formal contexts, International Journal of Machine Learning and Cybernetics 4(6): 721-731.
  • Li, B., Suna, X. and Leungc, H. (2013c). Combining concept lattice with call graph for impact analysis, Advances in Engineering Software 53: 41-43.
  • Li, J., Mei, C., Xu, W. and Qian, Y. (2015). Concept learning via granular computing: A cognitive viewpoint, Information Sciences 298: 447-467.
  • Li, M.Z. and Guo, L. (2013). Formal query systems on contexts and a representation of algebraic lattices, Information Sciences 239: 72-74.
  • Li, M.Z. and Mi, J.S. (2013). The strong direct product of formal contexts, Information Sciences 226: 47-67.
  • Li, S.T. and Tsai, F.C. (2013). A fuzzy conceptualization model for text mining with application in opinion polarity classification, Knowledge-Based Systems 39: 23-33.
  • Ma, J.M. and Zhang, W.X. (2013). Axiomatic characterizations of dual concept lattices, International Journal of Approximate Reasoning 54(5): 690-697.
  • Macko, J. (2013). User-friendly fuzzy FCA, in P. Cellier et al. (Eds.), Proceedings of the 11th International Conference ICFCA 2013, Lecture Notes in Computer Science, Vol. 7880, Springer, Berlin/Heidelberg, pp. 156-171.
  • Mariano, F.L., Asuncion, G.P. and Mari Carmen, S.F. (2013). Methodological guidelines for reusing general ontologies, Data and Knowledge Engineering 86: 242-275.
  • Martin, T.P., Abd Rahim, N.H. and Majidian, A. (2013). A general approach to the measurement of change in fuzzy concept lattices, Soft Computing 17(12): 2223-2234.
  • Martin, T. and Majidian, A. (2013). Finding fuzzy concepts for creative knowledge discovery, International Journal of Intelligent Systems 28(1): 93-114.
  • Massanet, S., Mayor, G., Mesiar, R. and Torrens, J. (2013). On fuzzy implications: An axiomatic approach, International Journal of Approximate Reasoning 54(9): 1471-1482.
  • Medina, J. (2012a). Relating attribute reduction in formal, object-oriented and property-oriented concept lattices, Computers and Mathematics with Applications 64(6): 1992-2002.
  • Medina, J. (2012b). Multi-adjoint property-oriented and object-oriented concept lattices, Information Sciences 190: 95-2006.
  • Medina, J. and Ojeda-Aciego, M. (2012). On multi-adjoint concept lattices based on heterogeneous conjunctors, Fuzzy Sets and Systems 208: 95-110.
  • Missaoui, R. and Kwuida, L. (2011). Mining triadic association rules from ternary relations, in P. Valtchev and R. Jäschke (Eds.), Proceedings of the 9th International Conference ICFCA 2011, Lecture Notes in Computer Science, Vol. 6628, Springer, Berlin/Heidelberg, pp. 204-218.
  • Muangprathub, J., Boonjing, V. and Pattaraintakorn, P. (2013). A new case-based classification using incremental concept lattice knowledge, Data and Knowledge Engineering 83: 39-53.
  • Muszyński, M. and Osowski, S. (2013). Data mining methods for gene selection on the basis of gene expression arrays, International Journal of Applied Mathematics and Computer Science 24(3): 657-668, DOI: 10.2478/amcs-2014-0048.
  • Neznanov, A. and Kuznetsov, S.O. (2013). Information retrieval and knowledge discovery with FCART, in S.O. Kuznetsov et al. (Eds.), Proceedings of FCAIR, Vol. 977, Moscow, pp. 74-82.
  • Nguyen, T.T., Hui, S.C and Chang, K. (2011). A lattice-based approach for mathematical search using formal concept analysis, Expert Systems with Applications 39(5): 5820-5828.
  • Nguyen, V.A. and Yamamoto, A. (2012). Learning from graph data by putting graphs on the lattice, Expert Systems with Applications 39(12): 11172-11182.
  • Obiedkov, S. (2012). Modeling preferences over attribute sets in formal concept analysis, in F. Domenach et al. (Eds.), Proceedings of the 10th International Conference ICFCA 2012, Lecture Notes in Computer Science, Vol. 7278, Springer, Berlin/Heidelberg, pp. 227-243.
  • Outrata, J. and Vychodil, V. (2012). Fast algorithm for computing fixpoints of Galois connections induced by object-attribute relational data, Information Sciences 185(1): 114-127.
  • Pavlovic, D. (2012). Quantitative concept analysis, in F. Domenach et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 7278, Springer, Berlin/Heidelberg, pp. 260-277.
  • Pedrycz, W. (2013). Granular Computing Analysis and Design of Intelligent Systems, CRC Press, Boca Raton, FL.
  • Pei, Z., Ruan, D., Meng, D. and Liu, Z. (2013). Formal concept analysis based on the topology for attributes of a formal context, Information Sciences 236: 66-82.
  • Pocs, J. (2012). On possible generalization of fuzzy concept lattices using dually isomorphic retracts, Information Sciences 210: 89-98.
  • Poelmans, J. (2011). Formally analyzing the concepts of domestic violence, Expert Systems with Applications 38(4): 3116-3130.
  • Poelmans, J., Ignatov, D.I., Kuznetsov, S.O. and Dedene, G. (2013a). Formal concept analysis in knowledge processing: A survey on models and techniques, Expert Systems with Applications 40(16): 6601-6623.
  • Poelmans, J., Kuznetsov, S.O., Ignatov, D.I. and Dedene, G. (2013b). Formal concept analysis in knowledge processing: A survey on applications, Expert Systems with Applications 40(16): 6538-6560.
  • Poelmans, J., Elzinga, P. and Dedene, G. (2013c). Retrieval of criminal trajectories with an FCA-based approach, in O. Kuznetsov et al. (Eds.), Proceedings of FCAIR, Vol. 977, Moscow, pp. 83-94.
  • Poelmans, J., Ignatov, D.I., Kuznetsov, S.O. and Dedene, G. (2014). Fuzzy and rough formal concept analysis: A survey, International Journal of General Systems 43(2): 105-134.
  • Poshyvanyk, D., Gethers, M. and Marcus, A. (2012). Concept location using formal concept analysis and information retrieval, ACM Transactions on Software Engineering and Methodology 21(4), Article No. 23, DOI:10.1145/2377656.2377660.
  • Priss, U. (2005). Linguistic applications of formal concept analysis, in B. Ganter et al. (Eds.), Formal Concept Analysis: Foundations and Applications, Lecture Notes in Computer Science, Vol. 3626, Springer, Berlin/Heidelberg, pp. 149-160.
  • Priss, U. (2006). Formal concept analysis in information science, Annual Review of Information Science and Technology 40(1): 521-543.
  • Priss, U. (2011). Unix systems monitoring with FCA, in S. Andrews et al. (Eds.), Conceptual Structures for Discovering Knowledge, Lecture Notes in Artificial Intelligence, Vol. 6828, Springer, Berlin/Heidelberg, pp. 243-256.
  • Priss, U. (2012). Concept lattices and median networks, Proceedings of CLA, Derby, UK, pp. 351-354.
  • Priss, U., Peter, R. and Jensen, N. (2012). Using FCA for modelling conceptual difficulties in learning processes, in S. Andrews et al. (Eds.), Conceptual Structures for Discovering Knowledge, Vol. 6828, Springer, Berlin/Heidelberg, pp. 161-173.
  • Priss, U., Jensen, N. and Rod, O. (2013). Using conceptual structures in the design of computer-based assessment software, in H.D. Pfeiffer et al. (Eds.), Conceptual Structures for Discovering Knowledge, Lecture Notes in Artificial Intelligence, Vol. 7735, Springer, Berlin/ Heidelberg, pp. 193-209.
  • Qin, X., Liu, K. and Tang, S. (2013). Fuzzy FCA-based web service discovery, Journal of Information and Computational Science 9(17): 5477-5484.
  • Rainer, B. and Ganapati, P. (2011). Formal concept analysis: Ranking and prioritization for multi-indicator systems, Environmental and Ecological Statistics 5: 117-133.
  • Radvansky, M., Sklenar, V. and Snasel, V. (2013). Evaluation of stream data by formal concept analysis, in M. Pechenizkiy and M. Wojciechowski (Eds.), New Trends in Databases and Information Systems, Advances in Intelligent Systems and Computing, Vol. 185, Springer, Berlin/Heidelberg pp. 131-140.
  • Romanov, V., Poluektova, A. and Sergienko, O. (2012). Adaptive EIS with business rules discovered by formal concept analysis, in C. Moller and S. Chaudhry (Eds.), Reconceptualizing Enterprise Information Systems, Lecture Notes in Business Information Processing, Vol. 105, Springer, Berlin/Heidelberg, pp. 105-117.
  • Rouane, H.M., Huchard, M., Napoli, A. and Valtchev, P. (2013). Relational concept analysis: Mining concept lattices from multi-relational data, Annals of Mathematics and Artificial Intelligence 67(1): 81-108.
  • Ruairi, de F. (2013). Formal concept analysis via atomic priming, in P. Cellier et al. (Eds.), Formal Concept Analysis, Lecture Notes and Computer Science, Vol. 7880, Springer, Berlin/Heidelberg, pp. 92-108.
  • Saquer, J. and Deogun, J.S. (2001). Concept approximations based on rough sets and similarity measures, International Journal of Applied Mathematics and Computer Science 11(3): 655-674.
  • Sarmah, A.K., Hazarika, S.M. and Sinha, S.K. (2015). Formal concept analysis: Current trends and directions, Artificial Intelligence Review 44: 47-86, DOI:10.1007/s10462-013-9404-0.
  • Sarnovsky, M., Butka, P. and Pocsova, J. (2012). Cloud computing as a platform for distributed fuzzy FCA approach in data analysis, Proceedings of the IEEE 16th International Conference on Intelligent Engineering Systems, Lisbon, Portugal, pp. 291-296.
  • Sawase, K., Nobuhara, H. and Bede, B. (2009). Visualizing huge image databases by formal concept analysis, Studies in Computational Intelligence 182: 291-296.
  • Sebastien, N., Fabien, P., Lotfi, L. and Rosine, C. (2013). The agree concept lattice for multidimensional database analysis, in P. Valtchev and R. Jäschke (Eds.), Formal Concept Analysis, Lecture Notes and Computer Science, Vol. 6628, Springer, Berlin/Heidelberg, pp. 219-234.
  • Senatore, S. and Pasi, G. (2013). Lattice navigation for collaborative filtering by means of (fuzzy) formal concept analysis, Proceedings of the 28th Annual ACM Symposium on Applied Computing, Coimbra, Portugal, pp. 920-926.
  • Shao, M.W., Leung, Y. and Wu, W.Z. (2014). Rule acquisition and complexity reduction in formal decision contexts, International Journal of Approximate Reasoning 55(1): 259-274.
  • Simiński, K. (2012). Neuro-rough-fuzzy approach for regression modelling from missing data, International Journal of Applied Mathematics and Computer Science 22(2): 461-476, DOI:10.2478/v10006-012-0035-4.
  • Singh, P.K. and Aswani Kumar, Ch. (2012a). Interval-valued fuzzy graph representation of concept lattice, Proceedings of the 12th ISDA, Kochi, India, pp. 604-609.
  • Singh, P.K. and Aswani Kumar, Ch. (2012b). A method for decomposition of fuzzy formal context, Procedia Engineering 38: 1852-1857.
  • Singh, P.K. and Aswani Kumar, Ch. (2014). Bipolar fuzzy graph representation of concept lattice, Information Sciences 288: 437-448.
  • Singh, P.K. and Aswani Kumar, Ch. (2015a). A note on computing the crisp order context of a fuzzy formal context for knowledge reduction, Journal of Information Processing Systems 11(2): 184-204.
  • Singh, P.K. and Aswani Kumar, Ch. (2015b). Analysis of composed contexts through projection, International Journal of Data Analysis Techniques and Strategies, (in press).
  • Singh, P.K., Aswani Kumar, Ch. and Li, J. (2015a). Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy, International Journal of Machine Learning and Cybernetics, DOI: 10.1007/s13042-014-0313-6.
  • Singh, P.K., Aswani Kumar, Ch. and Jinhai, Li (2015b). Knowledge representation using interval-valued fuzzy formal concept lattice, Soft Computing, DOI: 10.1007/s00500-015-1600-1.
  • Singh, P.K. and Gani, A. (2015). Fuzzy concept lattice reduction using Shannon entropy and Huffman coding, Journal of Applied Non-Classical Logics 25(2): 101-119, DOI: 10.1080/11663081.2015.1039857.
  • Slezak, D. (2012). Rough sets and FCA-Scalability challenges, in F. Domenach et al. (Eds.), Formal Concept Analysis, Lecture Notes and Computer Science, Vol. 7378, Springer, Berlin/Heidelberg, p. 6.
  • Spoto, A., Stefanutti, L. and Vidotto, G. (2010). Knowledge space theory, formal concept analysis, and computerized psychological assessment, Behavior Research Methods 42(1): 342-350.
  • Tadrat, J., Boonjing, V. and Pattaraintakorn, P. (2012). A new similarity measure in formal concept analysis for case-based reasoning, Expert Systems with Applications 39(1): 967-972.
  • Tang, P., Huia, S.C. and Fong, C.M.A. (2015). A lattice-based approach for chemical structural retrieval, Engineering Applications of Artificial Intelligence 39: 215-222.
  • Tho, Q.T., Hui, S.C. and Cao, T.H. (2006). Automatic fuzzy ontology generation for semantic web, IEEE Transactions on Knowledge and Data Engineering 18(6): 842-856.
  • Trabelsi, C., Jelassi, N. and Yahia, S.B. (2012). Scalable mining of frequent tri-concepts from Folksonomies, in P.-N. Tan et al. (Eds.), Advances in Knowledge Discovery and Data Mining, Lecture Notes and Computer Science, Vol. 7302, Springer, Berlin/Heidelberg, pp. 231-242.
  • Vityaev, E.E., Demin, A.V. and Ponomaryov, D.K. (2012). Probabilistic generalization of formal concepts, Programming and Computer Software 38(5): 219-230.
  • Wang, T.Z and Xu, H.S. (2011). Constructing domain ontology based on fuzzy set and concept lattice, Applied Mechanics and Materials 63-64: 715-718.
  • Wang, X. and Li, G. (2012). A similarity measure model based on rough concept lattice, in Y. Wu (Ed.), Software Engineering and Knowledge Engineering: Theory and Practice, Advances in Intelligent and Soft Computing, Vol. 114, Springer, Berlin/Heidelberg, pp. 99-103.
  • Wang, Y., Zhang, J. and Xu, H. (2012). The design of data collection methods in wireless sensor networks based on formal concept analysis, in D. Jin and S. Lin (Eds.), Advances in Computer Science and Information Engineering, Advances in Intelligent and Soft Computing, Vol. 169, Springer, Berlin/Heidelberg, pp. 33-38.
  • Watmough, M. (2014). Discovering the hidden semantics in enterprise resource planning data through formal concept analysis, Studies in Computational Intelligence 495: 291-314.
  • Wille, R. (1982). Restructuring lattice theory: An approach based on hierarchies of concepts, in I. Rival (Ed.), Ordered Sets, Reidel, Dordrecht/Boston, MA, pp. 445-470.
  • Wu, L., Qiua, D. and Mi, J.S. (2012). Automata theory based on complete residuated lattice-valued logic: Turing machines, Fuzzy Sets and Systems 208(12): 43-66.
  • Wu, W.Z., Leung, Y. and Mi, J.S. (2009). Granular computing and knowledge reduction in formal contexts, IEEE Transactions on Knowledge and Data Engineering 21(10): 1461-1474.
  • Xu, B., Frein, R.D., Robson, E. and Foghlu, M.O. (2012). Distributed formal concept analysis algorithms based on an iterative MapReduce framework, in F. Domenach et al. (Eds.), Formal Concept Analysis, Lecture Notes in Computer Science, Vol. 7278, Springer, Berlin/Heidelberg, pp. 292-308.
  • Xu, W. and Li, W. (2015). Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets, IEEE Transactions on Cybernetics 46(2): 366-379, DOI: 10.1109/TCYB.2014.2361772.
  • Yan, H., Zou, C., Liu, J. and Wang, Z. (2015). Formal concept analysis and concept lattice: Perspectives and challenges, International Journal of Autonomous and Adaptive Communications Systems 8(1): 81-96.
  • Yang, H. (2011). Formal concept analysis based on rough set theory and a construction algorithm of rough concept lattice, in H. Deng et al. (Eds.), Emerging Research in Artificial Intelligence and Computational Intelligence, Communications in Computer and Information Science, Vol. 237, Springer, Berlin/Heidelberg, pp. 239-244.
  • Yang, H.Z., Yee, L. and Shao, M.W. (2011a). Rule acquisition and attribute reduction in real decision formal contexts, Soft Computing 15(6): 1115-1128.
  • Yang, Y.P., Shieh, H.M., Tzeng, G.Z., Yen, L. and Shao, M.W. (2011b). Combined rough sets with flow graph and formal concept analysis for business aviation decision-making, Journal of Intelligent Information Systems 36(3): 347-366.
  • Yao, Y. (2004). A comparative study of formal concept analysis and rough set theory in data analysis, in S. Tsumoto et al. (Eds.), Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence, Vol. 3066, Springer, Berlin/Heidelberg, pp. 59-66.
  • Yao, Y., Mi, J., Li, Z. and Xie, B. (2012). The construction of fuzzy concept lattices based on (θ, σ)-fuzzy rough approximation operators, Fundamenta Informaticae 111(1): 33-45.
  • Yu, J., Hong, W., Li, S., Zhang, T. and Shao, M.W. (2013). A new approach of word sense disambiguation and knowledge discovery of English modal verbs by formal concept analysis, International Journal of Innovative Computing, Information and Control 9(3): 1189-1200.
  • Zerarga, L. and Djouadi, Y. (2013). Interval-valued fuzzy extension of formal concept analysis for information retrieval, in T. Huang et al. (Eds.), Neural Information Processing, Lecture Notes in Computer Science, Vol. 7663, Springer, Berlin/Heidelberg, pp. 608-615.
  • Zhai, Y., Li, D. and Qu, K. (2012). Probability fuzzy attribute implications for interval-valued fuzzy set, International Journal of Database Theory and Application 5(4): 95-108.
  • Zhai, Y., Li, D. and Qu, K. (2013). Fuzzy decision implications, Knowledge-Based Systems 37: 230-236.
  • Zhang, S., Guo, P., Zhang, J., Wang, X. and Pedrycz, W. (2012). A completeness analysis of frequent weighted concept lattices and their algebraic properties, Data and Knowledge Engineering 81-82: 104-117.
  • Zhang, L., Zhang, H., Shen, X. and Yin, L. (2013a). A bottom-up algorithm of vertical assembling concept lattices, International Journal of Data Mining and Bioinformatics 7(3): 229-244.
  • Zhang, Z., Du, J. and Yin, L. (2013b). Formal concept analysis approach for data extraction from a limited deep web database, Journal of Intelligent Information Systems 41(2): 1-24.
  • Zhao, J. and Liu, L. (2011). Construction of concept granule based on rough set and representation of knowledge-based complex system, Knowledge-Based Systems 24(6): 809-815.
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