Formal Concept Analysis in Knowledge Discovery: A Survey

  • Jonas Poelmans
  • Paul Elzinga
  • Stijn Viaene
  • Guido Dedene
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6208)


In this paper, we analyze the literature on Formal Concept Analysis (FCA) using FCA. We collected 702 papers published between 2003-2009 mentioning Formal Concept Analysis in the abstract. We developed a knowledge browsing environment to support our literature analysis process. The pdf-files containing the papers were converted to plain text and indexed by Lucene using a thesaurus containing terms related to FCA research. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. As a case study, we zoom in on the 140 papers on using FCA in knowledge discovery and data mining and give an extensive overview of the contents of this literature.


Formal Concept Analysis (FCA) knowledge discovery text mining exploratory data analysis systematic literature overview 


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  1. 1.
    Belohlavek, R., Vychodil, V.: Formal Concept Analysis With Background Knowledge: Attribute Priorities. IEEE Trans. Syst., man, & cyb. - C: App. & rev. 39(4) (2009)Google Scholar
  2. 2.
    Besson, J., Robardet, C., Boulicaut, J.F.: Mining a New Fault-Tolerant Pattern Type as an Alternative to Formal Concept Discovery. In: Schärfe, H., Hitzler, P., Øhrstrøm, P. (eds.) ICCS 2006. LNCS (LNAI), vol. 4068, pp. 144–157. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Beydoun, G.: Using Formal Concept Analysis towards Cooperative E-Learning. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS (LNAI), vol. 5465, pp. 109–117. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Beydoun, G., Kultchitsky, R., Manasseh, G.: Evolving semantic web with social navigation. Expert Systems with Applications 32, 265–276 (2007)CrossRefGoogle Scholar
  5. 5.
    Breu, S., Zimmermann, T., Lindig, C.: Mining Eclipse for Cross-Cutting Concerns. In: MSR 2006, Shanghai, China, May 22-23 (2006)Google Scholar
  6. 6.
    Carpineto, C., Romano, G.: Concept data analysis: Theory and applications. John Wiley & Sons, Chichester (2004)zbMATHCrossRefGoogle Scholar
  7. 7.
    Cellier, P., Ferré, S., Ridoux, O., Ducasse, M.: A parameterized algorithm to explore formal contexts with a taxonomy. Int. J. Found. of Comp. Sc. 19(2), 319–343 (2008)zbMATHCrossRefGoogle Scholar
  8. 8.
    Chang, Y.H.: Automatically constructing a domain ontology for document classification. In: 6th Int. Conf. on Machine Learning and Cybernetics, Hong Kong (2007)Google Scholar
  9. 9.
    Chou, C.Y., Mei, H.: Analyzing Tag-based Mashups with Fuzzy FCA. In: IEEE Int. Symposium on Service-Oriented System Engineering (2008)Google Scholar
  10. 10.
    Cole, R., Becker, P.: Navigation Spaces for the Conceptual Analysis of Software Structure. In: Ganter, B., Godin, R. (eds.) ICFCA 2005. LNCS (LNAI), vol. 3403, pp. 113–128. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Cole, R., Becker, P.: Navigation Spaces for the Conceptual Analysis of Software Structure. In: Ganter, B., Godin, R. (eds.) ICFCA 2005. LNCS (LNAI), vol. 3403, pp. 113–128. Springer, Heidelberg (2005)Google Scholar
  12. 12.
    Cole, R., Eklund, P., Stumme, G.: Document retrieval for e-mail search and discovery using Formal Concept Analysis. App. Art. Intel. 17, 257–280 (2003)CrossRefGoogle Scholar
  13. 13.
    Cole, R., Tilley, T., Ducrou, J.: Conceptual Exploration of Software Structure: A Collection of Examples. In: Belohlavek, R., Snasel, V. (eds.) CLA, pp. 135–148 (2005)Google Scholar
  14. 14.
    Correira, J.H., Stumme, G., Wille, R., Wille, U.: Conceptual knowledge discovery - a human-centered approach. Applied Artificial Intelligence 17, 281–302 (2003)CrossRefGoogle Scholar
  15. 15.
    Dau, F., Knechte, M.: Access Policy Design Supported by FCA Methods. In: Rudolph, S., Dau, F., Kuznetsov, S.O. (eds.) ICCS 2009. LNCS (LNAI), vol. 5662, pp. 141–154. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Del Grosso, C., Penta, M.D., Guzman, I.G.R.: An approach for mining services in database-oriented applications. In: 11th IEEE Eur. Conf. on Software Maintenance and Reeng. (2007)Google Scholar
  17. 17.
    Deogun, J., Jiang, L., Xie, Y., Raghavan, V.: Probability Logic Modeling of Knowledge Discovery in Databases. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 402–407. Springer, Heidelberg (2003)Google Scholar
  18. 18.
    Dong, G., Jiang, C., Pei, J., Li, J., Wong, L.: Mining Succinct Systems of Minimal Generators of Formal Concepts. In: Zhou, L.-z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 175–187. Springer, Heidelberg (2005)Google Scholar
  19. 19.
    Du, Y.J., Li, H.M.: Strategy for Mining Association Rules for Web Pages Based on Formal Concept Analysis. Elsevier, Amsterdam (2009), doi:10.1016/j.asoc.2009.09.007Google Scholar
  20. 20.
    Ducrou, J.: DVDSleuth: A Case Study in Applied Formal Concept Analysis for Navigating Web Catalogs. In: Priss, U., Polovina, S., Hill, R. (eds.) ICCS 2007. LNCS (LNAI), vol. 4604, pp. 496–500. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Eidenberger, H.: Visual Data Mining. In: Proceedings SPIE Optics East Conf., Philadelphia, October 26-28, vol. 5601, pp. 121–132 (2004)Google Scholar
  22. 22.
    Eisenbarth, T., Koschke, R., Simon, D.: Locating Features in Source Code. IEEE Transactions on Software Engineering 29(3) (March 2003)Google Scholar
  23. 23.
    Eklund, P., Ducrou, J.: Navigation and Annotation with Formal Concept Analysis. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS (LNAI), vol. 5465, pp. 118–121. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  24. 24.
    Eklund, P., Wille, R.: Semantology as Basis for Conceptual Knowledge Processing. In: Kuznetsov, S.O., Schmidt, S. (eds.) ICFCA 2007. LNCS (LNAI), vol. 4390, pp. 18–38. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  25. 25.
    Fenza, G., Loia, V., Senatore, S.: Concept Mining of Semantic Web Services By Means of Extended Fuzzy Formal Concept Analysis (FFCA). In: Int. Conf. Syst., Man & Cyb. (2008)Google Scholar
  26. 26.
    Fenza, G., Senatore, S.: Friendly web services selection exploiting fuzzy formal concept analysis. In: Soft Computing. Springer, Heidelberg (2009)Google Scholar
  27. 27.
    Fu, H.: Formal Concept Analysis for Digital Ecosystem. In: Proc. of the 5th Int. Conf. or Machine Learning and Applications (ICMLA 2006). IEEE, Los Alamitos (2006)Google Scholar
  28. 28.
    Ganter, B., Kuznetsov, S.O.: Formalizing Hypotheses with Concepts. In: Ganter, B., Mineau, G.W. (eds.) ICCS 2000. LNCS (LNAI), vol. 1867, pp. 342–356. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  29. 29.
    Ganter, B., Kuznetsov, S.O.: Scale Coarsening as Feature Selection. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 217–228. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  30. 30.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical foundations. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  31. 31.
    Gupta, A., Kumar, N., Bhatnagar, V.: Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis. In: Perner, P., Imiya, A. (eds.) MLDM 2005. LNCS (LNAI), vol. 3587, pp. 11–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  32. 32.
    Hamrouni, T., Yahia, S.B., Nguifo, E.M.: Towards a Finer Assessment of Extraction Contexts Sparseness. In: 18th Int. Workshop on Database and Expert Systems Applications (2007)Google Scholar
  33. 33.
    Hamrouni, T., Yahia, S.B., Slimani, Y.: Avoiding the itemset closure computation pitfall. In: Belohlavek, R., Snasel, V. (eds.) CLA 2005, pp. 46–59 (2005)Google Scholar
  34. 34.
    Hamrouni, T., Yahia, S.B., Slimani, Y.: Prince: An Algorithm for Generating Rule Bases Without Closure Computations. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. LNCS, vol. 3589, pp. 346–355. Springer, Heidelberg (2005a)CrossRefGoogle Scholar
  35. 35.
    Hashemi, R.R., De Agostino, S., Westgeest, B., Talburt, J.R.: Data Granulation and Formal Concept Analysis. In: Processing NAFIPS 2004, IEEE Annual Meeting of the Fuzzy Information, vol. 1, pp. 79–83 (2004)Google Scholar
  36. 36.
    He, H., Hai, H., Rujing, W.: FCA –Based Web User Profile Mining for Topics of Interest. In: Int. Conf. on Integation Technology, Shenzhen, China, March 20-24 (2007)Google Scholar
  37. 37.
    Hermann, M., Sertkaya, B.: On the Complexity of Computing Generators of Closed Sets. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 158–168. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  38. 38.
    Hsieh, T.C., Tsai, K.H., Chen, C.L., Lee, M.C., Chiu, T.K., Wang, T.: Query-based ontology approach for semantic search. In: 6th Int. Conf. on Machine Learning & Cyb. (2007)Google Scholar
  39. 39.
    Kaytone, M., Duplessis, S., Kuznetsov, S.O., Napoli, A.: Two FCA-Based Methods for Mining Gene Expression Data. In: Ferre, S., et al. (eds.) ICFCA. LNCS (LNAI), vol. 5548, pp. 251–266. Springer, Heidelberg (2009)Google Scholar
  40. 40.
    Keim, D.A.: Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics 8(I) (2002)Google Scholar
  41. 41.
    Kim, H.L., Hwang, S.H., Kim, H.G.: FCA-based Approach for Mining Contextualized Folksonomy. In: SAC 2007, Seoul, Korea, March 11-15 (2007)Google Scholar
  42. 42.
    Lakhal, L., Stumme, G.: Efficient Mining of Association Rules Based on Formal Concept Analysis. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3626, pp. 180–195. Springer, Heidelberg (2005)Google Scholar
  43. 43.
    Lei, Y., Cao, B., Yu, J.: A formal description-based approach to extended many-valued context analysis. In: 4th Conf. Fuzzy Systems and Knowledge Discovery, vol. 1, pp. 545–549 (2007)Google Scholar
  44. 44.
    Lim, W.C., Lee, C.S.: Knowledge Discovery Through Composited Visualization, Navigation and Retrieval. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds.) DS 2005. LNCS (LNAI), vol. 3735, pp. 377–379. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  45. 45.
    Maddouri, M.: Towards a machine learning approach based on incremental concept formation. Intelligent Data Analysis 8, 267–280 (2004)Google Scholar
  46. 46.
    Maddouri, M.: A Formal Concept Analysis Approach to Discover Association Rules from Data. In: Belohlavek, R., Snasel, V. (eds.) CLA, pp. 10-21 (2005)Google Scholar
  47. 47.
    Maddouri, M., Kaabi, F.: On Statistical Measures for Selecting Pertinent Formal Concepts to Discover Production Rules from Data. In: 6th Int. Conf. Data Mining Workshops (2006)Google Scholar
  48. 48.
    Meddouri, N., Maddouri, M.: Boosting Formal Concepts to Discover Classification Rules. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS (LNAI), vol. 5579, pp. 501–510. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  49. 49.
    Mens, K., Tourwe, T.: Delving source code with formal concept analysis. Computer Languages. Systems & Stuctures 3, 183–197 (2005)CrossRefGoogle Scholar
  50. 50.
    Molloy, I., Chen, H., Li, T., Wang, Q., Li, N., Bertino, E., Calo, S., Lobo, J.: Mining Roles with Semantic Meanings, Estes Park, Colorado, USA, June 11-13 (2008)Google Scholar
  51. 51.
    Myat, N.N., Hla, K.H.S.: A combined approach of formal concept analysis and text mining for concept based document clustering. In: Int. Conf. on Web Intelligence (2005)Google Scholar
  52. 52.
    Nehme, K., Valtchev, P., Rouane, M.H., Godin, R.: On Computing the Minimal Generator Family for Concept Lattices and Icebergs. In: Ganter, B., Godin, R. (eds.) ICFCA 2005. LNCS (LNAI), vol. 3403, pp. 192–207. Springer, Heidelberg (2005)Google Scholar
  53. 53.
    Nourine, L., Raynaud, O.: A fast algorithm for building lattices. In: Information Processing Letters, pp. 199–214 (1999)Google Scholar
  54. 54.
    Okubo, Y., Haraguchi, M.: Finding Conceptual Document Clusters with Improved Top-N Formal Concept Search. In: Int. Conf. on Web Intelligence (2006)Google Scholar
  55. 55.
    Pfaltz, J.C.: Representing numeric values in concept lattices. In: CLA (2007)Google Scholar
  56. 56.
    Poelmans, J., Elzinga, P., Viaene, S., Dedene, G.: A case of using formal concept analysis in combination with emergent self organizing maps for detecting domestic violence. In: Perner, P. (ed.) Advances in Data Mining. Applications and Theoretical Aspects. LNCS, vol. 5633, pp. 247–260. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  57. 57.
    Pogel, A., Ozonoff, D.: Contingency Structures and Concept Analysis. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 305–320. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  58. 58.
    Priss, U.: Formal Concept Analysis in Information Science. In: Blaise, C. (ed.) Annual Review of Information Science and Technology, ASIST, vol. 40 (2006)Google Scholar
  59. 59.
    Priss, U., Old, L.J.: Conceptual Exploration of Semantic Mirrors. In: Ganter, B., Godin, R. (eds.) ICFCA 2005. LNCS (LNAI), vol. 3403, pp. 21–32. Springer, Heidelberg (2005)Google Scholar
  60. 60.
    Qi, H., Liu, D.Y., Hu, C.Q., Lu, M., Zhao, L.: Searching for closed itemset with Formal Concept Analysis. In: 3rd Int. Conf. on Machine Learning and Cybernetics, Shanghai (2004)Google Scholar
  61. 61.
    Qu, L., Liu, D.: Extending Dynamic Aspect Mining Using Formal Concept Analysis. In: Proc. of 4th Conf. on Fuzzy Systems and Knowledge Discovery (2007)Google Scholar
  62. 62.
    Quan, T.T., Ngo, L.N., Hui, S.C.: An Effective Clustering-based Approach for Conceptual Association Rules Mining. In: Conf. Comp. & Comm. Techn., pp. 1–7 (2009)Google Scholar
  63. 63.
    Richards, D., Malik, U.: Multi level knowledge discovery from rule bases. Applied Artificial Intelligence 17, 181–205 (2003a)CrossRefGoogle Scholar
  64. 64.
    Richards, D.: Addressing the Ontology Acquisition Bottleneck through Reverse Ontological Engineering. Knowledge and Information Systems 6, 402–427 (2004)CrossRefGoogle Scholar
  65. 65.
    Richards, D.: Ad-Hoc and Personal Ontologies: A Prototyping Approach to Ontology Engineering. In: Hoffmann, A., Kang, B.-h., Richards, D., Tsumoto, S. (eds.) PKAW 2006. LNCS (LNAI), vol. 4303, pp. 13–24. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  66. 66.
    Richards, D., Malik, U.: Mining Propositional Knowledge Bases to Discover Multi-level Rules. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) MDM/KDD 2002 and KDMCD 2002. LNCS (LNAI), vol. 2797, pp. 199–216. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  67. 67.
    Rudolph, S.: Exploring Relational Structures Via FLE. In: Wolff, K.E., Pfeiffer, H.D., Delugach, H.S. (eds.) ICCS 2004. LNCS (LNAI), vol. 3127, pp. 196–212. Springer, Heidelberg (2004)Google Scholar
  68. 68.
    Rudolph, S.: Acquiring Generalized Domain-Range Restrictions. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 32–45. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  69. 69.
    Rudolph, S., Völker, J., Hitzler, P.: Supporting Lexical Ontology Learning by Relational Exploration. In: Priss, U., Polovina, S., Hill, R. (eds.) ICCS 2007. LNCS (LNAI), vol. 4604, pp. 488–491. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  70. 70.
    Sato, K., Okubo, Y., Haraguchi, M., Kunifuji, S.: Data Mining of Time-Series Medical Data by Formal Concept Analysis. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part II. LNCS (LNAI), vol. 4693, pp. 1214–1221. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  71. 71.
    Shao, M.W., Guo, Y.L.: Attribute reduction of large crisp-real concept lattices. In: Proc. of the 7th Int. Conf. on Machine Learning and Cybernetics, Kunming (2008)Google Scholar
  72. 72.
    Shi, B.S., Shen, X.J., Liu, Z.T.: A Knowledge discovery technique for heterogeneous data sources. In: Proc. of IEEE 2nd Int. Conf. on Machine Learning and Cybernetics, Xian (2003)Google Scholar
  73. 73.
    Sklenar, V., Zacpal, J., Sigmund, E.: Evaluation of IPAQ questionnaire by FCA. In: Belohlavek, R., Snasel, V. (eds.) CLA, pp. 60–69 (2005)Google Scholar
  74. 74.
    Stumme, G., Bestride, Y., Taouil, R., Lakhal, L.: Computing Iceberg Concept Lattices with TITANIC. Data and Knowledge Engineering 42(2), 189–222 (2002)zbMATHCrossRefGoogle Scholar
  75. 75.
    Stumme, G., Wille, R., Wille, U.: Conceptual knowledge discovery in databases using Formal Concept Analysis Methods. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 450–458. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  76. 76.
    Tekaya, S.B., Yahia, S.B., Slimani, Y.: GenAll Algorithm: Decorating Galois lattice with minimal generators. In: Belohlavek, R., Snasel, V. (eds.) CLA, pp. 166–178 (2005)Google Scholar
  77. 77.
    Thomas, J., Cook, K.: Illuminating the path: research and development agenda for visual analytics. National Visualization and Analytics Ctr. (2005)Google Scholar
  78. 78.
    Tilley, T.: Tool support for FCA. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 104–111. Springer, Heidelberg (2004)Google Scholar
  79. 79.
    Tilley, T., Eklund, P.: Citation analysis using Formal Concept Analysis: A case study in software engineering. In: 18th Int. Conf. on Database and Expert Systems Applications (2007)Google Scholar
  80. 80.
    Tonella, P., Ceccato, M.: Aspect Mining through the Formal Concept Analysis of Execution Traces. In: Proc. of the 11th Working Conf. on Reverse Engineering (2004)Google Scholar
  81. 81.
    Valtchev, P., Missaoui, R., Godin, R.: Formal Concept Analysis for Knowledge Discovery and Data Mining: The New Challenges. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 352–371. Springer, Heidelberg (2004)Google Scholar
  82. 82.
    Valtchev, P., Missaoui, R., Godin, R.: A framework for incremental generation of closed itemsets. Discrete Applied Mathematics 156, 924–949 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  83. 83.
    Valverde-Albacete, F.J., Pelaez-Moreno, C.: Galois Connections Between Semimodules and Applications in Data Mining. In: Kuznetsov, S.O., Schmidt, S. (eds.) ICFCA 2007. LNCS (LNAI), vol. 4390, pp. 181–196. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  84. 84.
    Valverde-Albacete, F.J., Pelaez-Moreno, C.: Spectral Lattices of Rmax,+-Formal Contexts. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 124–139. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  85. 85.
    Valverde-Albacete, F.J., Pelaez-Moreno, C.: Towards a generalization of Formal concept analysis for data mining purposes. In: Missaoui, R., Schmidt, J. (eds.) ICFCA 2006. LNCS (LNAI), vol. 3874, pp. 161–176. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  86. 86.
    Volker, J., Rudolph, S.: Fostering Web Intelligence by Semi-automatic OWL Ontology Refinement. In: Int. Conf. on Web Intelligence and Intelligent Agent Technology (2008)Google Scholar
  87. 87.
    Volker, J., Rudolph, S.: Lexico-Logical Acquisition of OWL DL Axioms: An Integrated Approach to Ontology Refinement. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 62–77. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  88. 88.
    Wang, H., Zhang, W.X.: Generalized attribute reduction in consistent decision formal context. In: 7th Int. Conf. on Machine Learning and Cybernetics, Kunming, July 12-15 (2008)Google Scholar
  89. 89.
    Wermelinger, M., Yu, Y., Strohmaier, M.: Using Formal Concept Analysis to Construct and Visualize Hierarchies of Socio-Technical Relations. In: ICSE 2009, Vancouver (2009)Google Scholar
  90. 90.
    Wille, R.: Methods of conceptual knowledge processing. In: Missaoui, R., Schmidt, J. (eds.) ICFCA 2006. LNCS (LNAI), vol. 3874, pp. 1–29. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  91. 91.
    Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered sets, pp. 445–470. Reidel, Dordrecht (1982)Google Scholar
  92. 92.
    Wollbold, J., Guthke, R., Ganter, B.: Constructing a Knowledge Base for Gene Regulatory Dynamics by Formal Concept Analysis Methods. In: Horimoto, K., Regensburger, G., Rosenkranz, M., Yoshida, H. (eds.) AB 2008. LNCS, vol. 5147, pp. 230–244. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  93. 93.
    Wu, W.Z., Leung, Y., Mi, J.S.: Granular Computing and Knowledqe Reduction in Formal Contexts. IEEE Transactions on Knowledge & Data Engineering 21(10) (October 2009)Google Scholar
  94. 94.
    Yahia, S.B., Xguifo, E.M.: Revisiting Generic Bases of Association Rules. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2004. LNCS, vol. 3181, pp. 58–67. Springer, Heidelberg (2004)Google Scholar
  95. 95.
    Yan, W., Baoxiang, C.: Fuzzy Many-Valued Context Analysis Based on Formal Description. In: 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (2007)Google Scholar
  96. 96.
    Yang, S.Z., Hou, X.W., Zhang, M.Q.: Approach on Aspect-Oriented Software Reverse Engineering at Requirements Level. In: Int. Conf. on Computer Science and Software Engineering. IEEE, Los Alamitos (2008)Google Scholar
  97. 97.
    Yang, Y., Du, Y., Sun, J., Hai, Y.: A Topic-Specific Web Crawler with Concept Similarity Context Graph Based on FCA. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 840–847. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  98. 98.
    Zarate, L.E., Dias, S.M.: Qualitative behavior rules for the cold rolling process extracted from trained ANN via the FCANN method. Engineering Applications of Artificial Intelligence 22, 718–731 (2009)CrossRefGoogle Scholar
  99. 99.
    Zhou, B., Hui, S.C., Chang, K.: A formal concept analysis approach for web usage mining. Intelligent Information Processing II 163, 437–441 (2005)CrossRefGoogle Scholar
  100. 100.
    Zhou, B., Hui, S.C., Fong, A.C.M.: An Effective Approach for Periodic Web Personalization. In: Proc. of the IEEE/WIC/ACM Int. Conf. on Web Intelligence (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jonas Poelmans
    • 1
  • Paul Elzinga
    • 3
  • Stijn Viaene
    • 1
    • 2
  • Guido Dedene
    • 1
    • 4
  1. 1.K.U.Leuven, Faculty of Business and EconomicsLeuvenBelgium
  2. 2.Vlerick Leuven Gent Management SchoolLeuvenBelgium
  3. 3.Amsterdam-Amstelland PoliceAmsterdamThe Netherlands
  4. 4.Universiteit van Amsterdam Business SchoolAmsterdamThe Netherlands

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