Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 11/2018

08.03.2018 | Original Article

Connections between two-universe rough sets and formal concepts

verfasst von: Ming-Wen Shao, Li Guo, Chang-Zhong Wang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 11/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Rough sets and formal concept analysis are two complementary tools during the process of data analysis. Two-universe rough set model is one of generalization of the classical rough sets. In this paper, the connections between two-universe rough sets and formal concepts are discussed. We investigate the relations between two-universe rough sets and the object (attribute) oriented formal concepts. We also establish connections between revised two-universe rough sets and the object (attribute) oriented formal concepts. Meanwhile, relations between algebraic characterizations of two-universe rough sets and formal concepts are revealed. Some properties of two-universe rough sets are examined.
Vorheriger Artikel L-fuzzy concept analysis for three-way decisions: basic definitions and fuzzy inference mechanisms
Nächster Artikel A new FCA-based method for identifying biclusters in gene expression data

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Literatur
1.
Belohlavek R, Vychodil V (2009) Formal concept analysis with background knowledge: attribute priorities. IEEE Trans Syst Man Cybern Cybern Part C Appl Rev 39(4):399–409CrossRef
2.
3.
Chen JK, Li JJ, Lin YJ, Lin GP, Ma ZM (2015) Relations of reduction between covering generalized rough sets and concept lattices. Inf Sci 304:16–27MathSciNetCrossRef
4.
Chen HM, Li TR, Ruan D, Lin JH, Hu CX (2012) A rough-set based incremental approach for updating approximations under dynamic maintenance environments. IEEE Trans Knowl Data Eng 25(2):274–284CrossRef
5.
Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17:191–208CrossRef
6.
Formica A (2012) Semantic web search based on rough sets and Fuzzy formal concept analysis. Knowl Based Syst 26:40–47CrossRef
7.
Ganter B, Wille R (1999) Formal concept analysis, mathematic foundations. Springer, BerlinCrossRef
8.
Duntsch N, Gediga G (2002) Modal-style operators in qualitative data analysis. In: Proceedings of the 2002 IEEE international conference on data mining, vol 18. IEEE, pp 155–162
9.
Hashem T, Ahmed CF, Samiullah M et al (2014) Assessing semantic annotation activities with formal concept analysis. Expert Syst Appl 41:2914–2938CrossRef
10.
Hu QH, Yu DR, Liu JF, Wu CX (2008) Neighborhood rough set based heterogeneous feature selection. Inf Sci 178:3577–3594MathSciNetCrossRef
11.
Hu QH, Pedrgcz W, Yu DR, Lang J (2010) Selecting discrete and continuous feasures based on neighborhood decision error minimization. IEEE Trans Syst Man Cybern C Part B Cybern 40:137–150CrossRef
12.
Kang XP, Li DY, Wang SG, Qu KS (2012) Formal concept analysis based on fuzzy granularity base for different granulations. Fuzzy Sets Syst 203:33–48MathSciNetCrossRef
13.
Kaytoue M, Kuznetsov SO, Napoli A, Duplessis S (2011) Mining gene expression data with pattern structures in formal concept analysis. Inf Sci 181:1989–2001MathSciNetCrossRef
14.
Lai HL, Zhang DX (2009) Concept lattices of fuzzy contexts: formal concept analysis vs. rough set theory. Int J Approx Reason 50:695–707MathSciNetCrossRef
15.
16.
Liang JY, Zhao XW, Li DY, Cao FY, Dang CY (2012) Determining the number of clusters using information entropy for mixed data. Pattern Recogn 45:2251–2265CrossRef
17.
Li JH, Mei CL, Lv YJ (2013) Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. Int J Approx Reason 54(1):149–165MathSciNetCrossRef
18.
Li JH, Mei CL, Xu WH, Qian YH (2015) Concept learning via granular computing: a cognitive viewpoint. Inf Sci 298:447–467MathSciNetCrossRef
19.
Li JH, Ren Y, Mei CL, Qian YH, Yang XB (2016) A comparative study of multigranulation rough sets and concept lattices via rule acquisition. Knowl Based Syst 91:152–164CrossRef
20.
Liu GL (2010) Rough set theory based on two universal sets and its applications. Knowl Based Syst 23(2):110–115CrossRef
21.
Liu M, Shao MW, Zhang WX, Wu C (2007) Reduction method for concept lattices based on rough set theory and its application. Comput Math Appl 53(9):1390–1410MathSciNetCrossRef
22.
23.
Pawlak Z (1991) Rough Sets. Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, DordrechtCrossRef
24.
Pedrycz W, Bargiela A (2002) Granular clustering: a granular signature of data. IEEE Trans Syst Man Cybern Part A 32(2):212–224CrossRef
25.
26.
27.
Qian YH, Liang JY, Pedrycz W, Dang CY (2010) Positive approximation: an accelerator for attribute reduction in rough set theory. Artif Intell 174:597–618MathSciNetCrossRef
28.
Qi JJ, Qian T, Wei L (2016) The connections between three-way and classical concept lattices. Knowl Based Syst 91:143–151CrossRef
29.
Shao MW, Liu M, Zhang WX (2007) Set approximations in fuzzy formal concept analysis. Fuzzy Set Syst 158:2627–2640MathSciNetCrossRef
30.
Shao MW, Leung Y (2014) Relations between granular reduct and dominance reduct in formal contexts. Knowl Based Syst 65:1–11CrossRef
31.
Shao MW, Leung Y, Wang XZ, Wu WZ (2016) Granular reducts of formal fuzzy contexts. Knowl Based Syst 114:156–166CrossRef
32.
She YH, Wang GJ (2009) An axiomatic approach of fuzzy rough sets based on residuated lattices. Comput Math Appl 58(1):189–201MathSciNetCrossRef
33.
Kumar CA, Srinivas S (2010) Concept lattice reduction using fuzzy K-means clustering. Expert Syst Appl 37:2696–2704CrossRef
34.
Tan AH, li JJ, Lin GP (2015) Connections between covering-based rough sets and concept lattices. Int J Approx Reason 56:43–58MathSciNetCrossRef
35.
Tsumoto S (2003) Automated extraction of hierarchical decision rules from clinical databases using rough set model. Expert Syst Appl 24(2):189–197CrossRef
36.
Wang C, Qi Y, Shao M, Hu Q, Chen D, Qian Y, Lin Y (2017) A fitting model for feature selection with fuzzy rough sets. IEEE Trans Fuzzy Syst 25(4):741–753CrossRef
37.
38.
Wang H, Zhang WX (2006) Relationships between concept lattice and rough set. Lect Notes Artif Intell 4029:538–547
39.
Wang X, Zhang WX (2008) Relations of attribute reduction between object and property oriented concept lattices. Knowl Based Syst 21(5):398–403CrossRef
40.
Wang X (2012) Approaches to attribute reduction in concept lattices based on rough set theory. Int J Hybrid Inf Technol 5(2):67–79
41.
Wei L, Qi JJ (2010) Relation between concept lattice reduction and rough set reduction. Knowl Based Syst 23:934–938CrossRef
42.
Wei L, Wan Q (2016) Granular transformation and irreducible element judgment theory based on pictorial diagrams. IEEE Trans Cybern 46(2):380–387CrossRef
43.
Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered sets. Reidel, Dordrecht, Boston, pp 445–470CrossRef
44.
Wu Q, Liu ZT (2009) Real formal concept analysis based on grey-rough set theory. Knowl Based Syst 22:38–45CrossRef
45.
46.
Wu WZ, Zhang WX (2004) Constructive and axiomatic approaches of fuzzy approximation operators. Inf Sci 159:233–254MathSciNetCrossRef
47.
Zhang XH, Miao DQ, Liu CH, Le ML (2016) Constructive methods of Rough approximation operators and multigranulation rough sets. Knowl Based Syst 91:114–125CrossRef
48.
49.
Zhu W, Wang FY (2007) On three types of covering rough sets. IEEE Trans Knowl Data Eng 19:1131–1144CrossRef
50.
Yao YY, Wong SKM, Wang LS (1995) A non-numeric approach to uncertain reasoning. Int J Gen Syst 23:343–359CrossRef
51.
52.
Yao YY (1998) Generalized rough set model. In: Polkowski L, Skowron A (eds) Rough sets in knowledge discovery. Methodology and applications, vol 1. Physica-Verlag, Heidelberg, pp 286–318
53.
Yao YY (2004) Concept lattices in rough set theory. In: Dick S, Kurgan L, Pedrycz W, Reformat M (eds) Proceedings of 2004 annual meeting of the North American fuzzy information processing society, vol 2. IEEE, pp 73–78
54.
Yao YY (2004) A comparative study of formal concept analysis and rough set theory in data analysis. In: Tsumoto S, Slowinski R, Jan Komorowski H, Grzymala-Busse JW (eds) 4th International conference on rough sets and current trends in computing. Lecture notes in computer science, vol 3066. Springer, Berlin, Heidelberg, pp 59–68CrossRef
55.
56.
Zhou L, Wu WZ (2008) On generalized intuitionistic fuzzy rough approximation operators. Inf Sci 178:2448–2465MathSciNetMATH
Metadaten
Titel
Connections between two-universe rough sets and formal concepts
verfasst von
Ming-Wen Shao
Li Guo
Chang-Zhong Wang
Publikationsdatum
08.03.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 11/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0803-z

Weitere Artikel der Ausgabe 11/2018

International Journal of Machine Learning and Cybernetics 11/2018 Zur Ausgabe

Editorial

Three-way decisions, concept lattice and granular computing: Editorial

Original Article

Synthesizing decision rules from multiple information sources: a neighborhood granulation viewpoint

Original Article

Feature selection based on maximal neighborhood discernibility

Original Article

Three-way n-valued neutrosophic concept lattice at different granulation

Original Article

Relation granulation and algebraic structure based on concept lattice in complex information systems

Original Article

L-fuzzy concept analysis for three-way decisions: basic definitions and fuzzy inference mechanisms

Neuer Inhalt

    Bildnachweise