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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 1/2019

18.05.2018 | Regular Paper

Expert deduction rules in data mining with association rules: a case study

verfasst von: Jan Rauch

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2019

Einloggen

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

search-config
loading …

Abstract

An approach to dealing with domain knowledge in data mining with association rules is introduced. We deal with association rules with remarkably enhanced syntax. This opens various possibilities for both logical and expert deduction. An expert deduction rule is a logically incorrect deduction rule which is supported by an indisputable fact concerning the application domain. The expert deduction rule is correct according to the given indisputable fact if a suitable assertion related to the given expert deduction rule can be formally proved from this indisputable fact. Examples of expert deduction rules and their applications are presented.
Vorheriger Artikel A comprehensive empirical comparison of hubness reduction in high-dimensional spaces
Nächster Artikel A smoothed monotonic regression via L2 regularization

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 "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

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
Literatur
1.
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Bocca JB, Jarke M, Zaniolo C (eds) Proceedings of 20th international conference on very large data bases, VLDB, vol 1215. Morgan Kaufmann, San Francisco, pp 487–499
2.
Brossette SE, Sprague AP, Hardin JM, Waites KB, Jones WT, Moser SA (1998) Association rules and data mining in hospital infection control and public health surveillance. J Am Med Inf Assoc (JAMIA) 5(4):373–381CrossRef
3.
Delgado M, Sanchez D, Martin-Bautista MJ, Vila MA (2001) Mining association rules with improved semantics in medical databases. Artif Intell Med 21(1–3):241–245CrossRef
4.
Fürnkranz J, Kliegr T (2015) A brief overview of rule learning. In: Bassiliades N, Gottlob G, Sadri F, Paschke A, Roman D (eds)In:9th international symposium on foundations, tools, and application, RuleML 2015. LNCS, vol 9202. Springer, Heidelberg, pp 56–69
5.
Geng L, Hamilton HJ (2006) Interestingness measures for data mining: a survey. ACM Comput Surv (CSUR) 38:1–32CrossRef
6.
7.
Brin S, Rastogi R, Kyuseok S (2003) Mining optimized gain rules for numeric attributes. IEEE Trans Knowl Data Eng 15(2):324–338CrossRef
8.
Fukuda T, Morimoto Y, Morishita S, Tokuyama T (1999) Mining optimized association rules for numeric attributes. J Comput Syst Sci 58(1):1–12MathSciNetCrossRefMATH
9.
Gasmi G, Yahia SB, Nguifo EM, Bouker S (2007) Extraction of Association Rules Based on Literalsets. In: Song IY, EderTho J, Nguyen M (eds) In: DaWaK 2007. LNCS, vol 4654. Springer, Heidelberg, pp 293–302
10.
Hájek P (1978) (guest ed. ) International Journal of Man-Machine Studies, special issue on GUHA. 10
11.
Hájek P (1984) The new version of the GUHA procedure ASSOC. In: Havranek T, Sidak Z, Novak M (eds) In: COMPSTAT 1984. Springer, Heidelberg, pp 360–365
12.
Hamrouni T, Yahia BS, Nguifo EM (2010) Generalization of association rules through disjunction. Ann Math Artif Intell 59(2):201–222MathSciNetCrossRefMATH
13.
Hájek P, Havel I, Chytil M (1966) The GUHA method of automatic hypotheses determination. Computing 1:293–308CrossRefMATH
14.
15.
Hájek P, Sochorová A, Zvárová J (1995) GUHA for personal computers. Comput Stat Data Anal 19:149–153CrossRefMATH
16.
17.
Idoudi R, Ettaba KS, Solaiman B, Hamrouni K (2016) Ontology knowledge mining based association rules ranking. In: Howlett RJ, Jain CL, Gabrys B, Toro C, Lim CP (eds) Knowledge-based and intelligent information & engineering systems. Procedia computer science, vol 96. Elsevier, Amsterdam, pp 345–354
18.
Mansingh G, Osei-Bryson K-M, Reichgelt H (2011) Using ontologies to facilitate post-processing of association rules by domain experts. Inf Sci 181(3):419–434CrossRef
19.
Marinica C, Guillet F (2010) Knowledge-based interactive postmining of association rules using ontologies. IEEE Trans Knowl Data Eng 22(6):784–797CrossRef
20.
Minaei-Bidgoli B, Barmaki R, Nasiri M (2013) Mining numerical association rules via multi-objective genetic algorithms. Inf Sci 233:15–24CrossRef
21.
Ordonez C, Ezquerra N, Santana CA (2006) Constraining and summarizing association rules in medical data. Knowl Inf Syst (KAIS) 9(3):259–283CrossRef
22.
Personeni G, Bresso E, Devignes M, Dumontier M, Smaïl-Tabbone M, Coulet A (2017) Discovering associations between adverse drug events using pattern structures and ontologies. J Biomed Semant 8(1):29:1–29:13CrossRef
23.
Qiang Y, Xindong W (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Mak 5(4):597–604CrossRef
24.
Brin S, Rastogi R, Kyuseok S (1999) Mining optimized gain rules for numeric attributes. In: Usama Fayyad U, Chaudhuri S, Madigan D (eds) Proceedings on fifth ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, pp 135–144
25.
Ralbovský M, Kuchař T (2007) Using disjunctions in association mining. In: Perner P (ed) Proceedings on advances in data mining—theoretical aspects and applications, LNCS, vol 4597. Springer, Berlin, pp 339–351
26.
27.
28.
Rauch J (2015) Formal framework for data mining with association rules and domain knowledge overview of an approach. Fund Inf 137:171–217MathSciNetMATH
29.
Rauch J (2016) Logical aspects of dealing with domain knowledge in data mining with association rules. Fund Inf 148:1–33MathSciNetMATH
30.
Rauch J, Šim\(\mathring{{\rm u}}\)nek M (2014) Learning association rules from data through domain knowledge and automation. In: Bikakis A, Fodor P, Roman D (eds.) RuleML 2014: Rules on the Web. From Theory to Applications. LNCS, vol. 8620, Springer, Heidelberg, pp 266–280
31.
Rauch J, Šim\(\mathring{{\rm u}}\)nek M (2017) Apriori and GUHA comparing two approaches to data mining with association rules. Intell Data Anal 21:981–1013
32.
Regulski K (2017) Formalization of technological knowledge in the field of metallurgu using document classification tools supported with semantic techniques. Arch Metall Mater 62(2):715–720CrossRef
33.
Singh V, Nagpal S (2010) Integrating users domain knowledge with association rule mining. Int J Comput Sci Issues 7(2):30–34
34.
Srikant R, Agrawal R (1997) Mining generalized association rules. Future Gen Comput Syst 13(2–3):161–180CrossRef
35.
Metadaten
Titel
Expert deduction rules in data mining with association rules: a case study
verfasst von
Jan Rauch
Publikationsdatum
18.05.2018
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 1/2019
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-018-1206-x

Weitere Artikel der Ausgabe 1/2019

Knowledge and Information Systems 1/2019 Zur Ausgabe

Regular Paper

RTCRelief-F: an effective clustering and ordering-based ensemble pruning algorithm for facial expression recognition

Regular Paper

Fast detection of community structures using graph traversal in social networks

Regular Paper

Fast crawling methods of exploring content distributed over large graphs

Regular Paper

A large margin time series nearest neighbour classification under locally weighted time warps

Regular Paper

A smoothed monotonic regression via L2 regularization

Regular Paper

Multi-label classification using stacked hierarchical Dirichlet processes with reduced sampling complexity