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1. Formal Concept Analysis and Extensions for Complex Data Analytics

verfasst von : Léonard Kwuida, Rokia Missaoui

Erschienen in: Complex Data Analytics with Formal Concept Analysis

Verlag: Springer International Publishing

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Abstract

The goal of this paper is to first recall the key notions of Formal Concept Analysis and its main extensions, and then to give a brief overview of studies on complex data analytics. The latter refers to the analysis of complex data to discover patterns and learning models from data with a complex structure such as XML or Json data, texts, images, graphs, trees, multidimensional and streaming data. Finally, it presents the contributions inside this volume.

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Nächstes Kapitel Conceptual Navigation in Large Knowledge Graphs

We write (X _j, X _k) ⊆ K _j×K _k to mean that X _j ⊆ K _j and X _k ⊆ K _k.

OAC stands for Object-Attribute-Condition, while OA is a shorthand for Object-Attribute.

Babin, M.A., Kuznetsov, S.O.: Approximating concept stability. In: Formal Concept Analysis - 10th International Conference, ICFCA 2012, Leuven, Belgium, May 7–10, 2012. Proceedings. pp. 7–15 (2012)

Belohlávek, R.: Concept lattices and order in fuzzy logic. Ann. Pure Appl. Log. 128(1–3), 277–298 (2004)MathSciNetCrossRef

Belohlávek, R.: What is a fuzzy concept lattice? II. In: Kuznetsov, S.O., Slezak, D., Hepting, D.H., Mirkin, B.G. (eds.) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing - 13th International Conference, RSFDGrC 2011, Moscow, Russia, June 25–27, 2011. Proceedings. Lecture Notes in Computer Science, vol. 6743, pp. 19–26. Springer (2011). https://doi.org/10.1007/978-3-642-21881-1_4

Belohlavek, R., De Baets, B., Konecny, J.: Granularity of attributes in formal concept analysis. Information Sciences 260, 149–170 (2014)MathSciNetCrossRef

Belohlávek, R., Trnecka, M.: Basic level in formal concept analysis: Interesting concepts and psychological ramifications. In: IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3–9, 2013. pp. 1233–1239 (2013)

Besson, J., Pensa, R.G., Robardet, C., Boulicaut, J.F.: Constraint-based mining of fault-tolerant patterns from boolean data. In: International Workshop on Knowledge Discovery in Inductive Databases. pp. 55–71. Springer (2005)

Biedermann, K.: How triadic diagrams represent conceptual structures. In: ICCS. pp. 304–317 (1997)

Bifet, A., Gavaldà, R., Holmes, G., Pfahringer, B.: Machine Learning for Data Streams with Practical Examples in MOA. MIT Press (2018)

Cerf, L., Besson, J., Robardet, C., Boulicaut, J.F.: Closed patterns meet n-ary relations. TKDD 3(1) (2009)

10.

Cordeiro, R.L.F., Faloutsos, C., Jr., C.T.: Data Mining in Large Sets of Complex Data. Springer Briefs in Computer Science, Springer (2013)

11.

Dias, S.M., Vieira, N.J.: A methodology for analysis of concept lattice reduction. Inf. Sci. 396, 202–217 (2017)MathSciNetCrossRef

12.

Dmitry I. Ignatov, Dmitry V. Gnatyshak, Sergei O. Kuznetsov, and Boris Mirkin. Triadic formal concept analysis and triclustering: searching for optimal patterns. Machine Learning, pages 1–32, 2015.

13.

Dmitry I. Ignatov, Sergei O. Kuznetsov, Jonas Poelmans, and Leonid E. Zhukov. Can triconcepts become triclusters? International Journal of General Systems, 42(6):572–593, 2013.MathSciNetCrossRef

14.

Dmitry I. Ignatov, Sergei O. Kuznetsov, and Jonas Poelmans. Concept-based biclustering for internet advertisement. In ICDM Workshops, pages 123–130. IEEE Computer Society, 2012.

15.

Fan, W., Bifet, A.: Mining big data: current status, and forecast to the future. SIGKDD Explorations 14(2), 1–5 (2012)CrossRef

16.

Ferré, S., Cellier, P.: Graph-fca: An extension of formal concept analysis to knowledge graphs. Discret. Appl. Math. 273, 81–102 (2020)MathSciNetCrossRef

17.

Ferré, S., Ridoux, O.: A logical generalization of formal concept analysis. In: International Conference on Conceptual Structures: Logical, Linguistic, and Computational Issues. pp. 371–384. Springer (2000)

18.

Ferré, S., Ridoux, O.: Introduction to logical information systems. Inf. Process. Manag. 40(3), 383–419 (2004)CrossRef

19.

Gaber, M.M.: Advances in data stream mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2(1), 79–85 (2012)CrossRef

20.

Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: International conference on conceptual structures. pp. 129–142. Springer (2001)

21.

Ganter, B., Obiedkov, S.A.: Conceptual Exploration. Springer (2016)

22.

Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer-Verlag New York, Inc. (1999), translator-C. Franzke

23.

Guigues, J.L., Duquenne, V.: Familles minimales d’implications informatives résultant d’un tableau de données binaires. Mathématiques et Sciences humaines 95, 5–18 (1986)

24.

Hacene, M.R., Huchard, M., Napoli, A., Valtchev, P.: Relational concept analysis: mining concept lattices from multi-relational data. Ann. Math. Artif. Intell. 67(1), 81–108 (2013)MathSciNetCrossRef

25.

Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edition. Morgan Kaufmann (2011)

26.

Horak, Z., Kudelka, M., Snásel, V.: Properties of concept lattice reduction based on matrix factorization. In: IEEE International Conference on Systems, Man, and Cybernetics, Manchester, SMC 2013, United Kingdom, October 13–16, 2013. pp. 333–338. IEEE (2013)

27.

Huchard, M., Hacene, M.R., Roume, C., Valtchev, P.: Relational concept discovery in structured datasets. Annals of Mathematics and Artificial Intelligence 49(1–4), 39–76 (2007)MathSciNetCrossRef

28.

Ibrahim, M.H., Missaoui, R.: Approximating concept stability using variance reduction techniques. Discret. Appl. Math. 273, 117–135 (2020)MathSciNetCrossRef

29.

Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. In: Braslavski, P., Karpov, N., Worring, M., Volkovich, Y., Ignatov, D.I. (eds.) Information Retrieval - 8th Russian Summer School, RuSSIR 2014, Nizhniy, Novgorod, Russia, August 18–22, 2014, Revised Selected Papers. Communications in Computer and Information Science, vol. 505, pp. 42–141. Springer (2014). https://doi.org/10.1007/978-3-319-25485-2_3

30.

Ignatov, D.I., Kwuida, L.: Interpretable concept-based classification with shapley values. In: Alam, M., Braun, T., Yun, B. (eds.) Ontologies and Concepts in Mind and Machine - 25th International Conference on Conceptual Structures, ICCS 2020, Bolzano, Italy, September 18–20, 2020, Proceedings. Lecture Notes in Computer Science, vol. 12277, pp. 90–102. Springer (2020). https://doi.org/10.1007/978-3-030-57855-8_7

31.

Klimushkin, M., Obiedkov, S.A., Roth, C.: Approaches to the selection of relevant concepts in the case of noisy data. In: ICFCA. vol. 20, pp. 255–266. Springer (2010)

32.

Kuznetsov, S.O., Makhalova, T.P.: On interestingness measures of formal concepts. Inf. Sci. 442–443, 202–219 (2018)MathSciNetCrossRef

33.

Kuznetsov, S.O., Poelmans, J.: Knowledge representation and processing with formal concept analysis. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3(3), 200–215 (2013)CrossRef

34.

Kwuida, L., Missaoui, R., Balamane, A., Vaillancourt, J.: Generalized pattern extraction from concept lattices. Annals of Mathematics and Artificial Intelligence 72(1–2), 151–168 (2014)MathSciNetCrossRef

35.

Lehmann, F., Wille, R.: A Triadic Approach to Formal Concept Analysis. In: Proceedings of the Third International Conference on Conceptual Structures: Applications, Implementation and Theory. pp. 32–43 (1995)

36.

L’Heureux, A., Grolinger, K., ElYamany, H.F., Capretz, M.A.M.: Machine learning with big data: Challenges and approaches. IEEE Access 5, 7776–7797 (2017)CrossRef

37.

Medina, J., Ruiz-Calviño, J.: Fuzzy formal concept analysis via multilattices: First prospects and results. In: Szathmary, L., Priss, U. (eds.) Proceedings of The Ninth International Conference on Concept Lattices and Their Applications, Fuengirola (Málaga), Spain, October 11–14, 2012. CEUR Workshop Proceedings, vol. 972, pp. 69–80. CEUR-WS.org (2012),

38.

Park, H., Park, C., Kang, U.: Pegasusn: A scalable and versatile graph mining system. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2–7, 2018. pp. 8214–8215. AAAI Press (2018)

39.

Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Fuzzy and rough formal concept analysis: a survey. Int. J. Gen. Syst. 43(2), 105–134 (2014)MathSciNetCrossRef

40.

Prediger, S.: Logical scaling in formal concept analysis. In: International Conference on Conceptual Structures. pp. 332–341. Springer (1997)

41.

Prediger, S., Stumme, G.: Theory-driven logical scaling. In: International Workshop on Description Logics. vol. 22 (1999)

42.

Prediger, S., Wille, R.: The lattice of concept graphs of a relationally scaled context. In: International Conference on Conceptual Structures. pp. 401–414. Springer (1999)

43.

Raj, P., Raman, A., Nagaraj, D., Duggirala, S.: High-Performance Big-Data Analytics - Computing Systems and Approaches. Computer Communications and Networks, Springer (2015)

44.

Rouane, M.H., Huchard, M., Napoli, A., Valtchev, P.: A proposal for combining formal concept analysis and description logics for mining relational data. In: International Conference on Formal Concept Analysis. pp. 51–65. Springer (2007)

45.

Scheich, P., Skorsky, M., Vogt, F., Wachter, C., Wille, R.: Conceptual data systems. In: Information and classification, pp. 72–84. Springer (1993)

46.

Stumme, G.: Conceptual on-line analytical processing. In: Information Organization and Databases, pp. 191–203. Springer (2000)

47.

Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing iceberg concept lattices with titanic. Data & knowledge engineering 42(2), 189–222 (2002)CrossRef

48.

Sun, Y., Han, J.: Mining Heterogeneous Information Networks: Principles and Methodologies. Synthesis Lect. on Data Mining and Knowledge Discovery, Morgan & Claypool Pub. (2012)

49.

Szathmary, L., Valtchev, P., Napoli, A., Godin, R., Boc, A., Makarenkov, V.: A fast compound algorithm for mining generators, closed itemsets, and computing links between equivalence classes. Ann. Math. Artif. Intell. 70(1–2), 81–105 (2014)MathSciNetCrossRef

50.

Torim, A., Lindroos, K.: Sorting concepts by priority using the theory of monotone systems. In: International Conference on Conceptual Structures. pp. 175–188. Springer (2008)

51.

Ventos, V., Soldano, H.: Alpha galois lattices: An overview. In: International Conference on Formal Concept Analysis. pp. 299–314. Springer (2005)

52.

Voutsadakis, G.: Polyadic concept analysis. Order 19(3), 295–304 (2002)MathSciNetCrossRef

53.

Wille, R.: The basic theorem of triadic concept analysis. Order 12(2), 149–158 (1995)MathSciNetCrossRef

54.

Wu, J., Pan, S., Zhou, C., Li, G., He, W., Zhang, C.: Advances in processing, mining, and learning complex data: From foundations to real-world applications. Complex. 2018, 1–3 (2018)

55.

Wu, X., Zhu, X., Wu, G., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)CrossRef

56.

Zadeh, L.A.: Fuzzy sets. Inf. Control. 8(3), 338–353 (1965). https://doi.org/10.1016/S0019-9958(65)90241-X CrossRef

57.

Zaki, M.J., Hsiao, C.J.: Efficient algorithms for mining closed itemsets and their lattice structure. IEEE transactions on knowledge and data engineering 17(4), 462–478 (2005)CrossRef

Titel: Formal Concept Analysis and Extensions for Complex Data Analytics
verfasst von: Léonard Kwuida
Rokia Missaoui
Verlag: Springer International Publishing
Buch: Complex Data Analytics with Formal Concept Analysis
Print ISBN: 978-3-030-93277-0

Electronic ISBN: 978-3-030-93278-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-93278-7_1

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