nach oben

Soft Computing

Erschienen in:

01.05.2012 | Focus

Mining fuzzy association rules from low-quality data

verfasst von: A. M. Palacios, M. J. Gacto, J. Alcalá-Fdez

Erschienen in: Soft Computing | Ausgabe 5/2012

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Data mining is most commonly used in attempts to induce association rules from databases which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Different studies have proposed methods for mining association rules from databases with crisp values. However, the data in many real-world applications have a certain degree of imprecision. In this paper we address this problem, and propose a new data-mining algorithm for extracting interesting knowledge from databases with imprecise data. The proposed algorithm integrates imprecise data concepts and the fuzzy apriori mining algorithm to find interesting fuzzy association rules in given databases. Experiments for diagnosing dyslexia in early childhood were made to verify the performance of the proposed algorithm.

Vorheriger Artikel Missing data imputation for fuzzy rule-based classification systems

Nächster Artikel New algorithms for finding approximate frequent item sets

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

Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: SIGMOD, Washington, D.C., USA, pp 207–216

Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: International conference on very large data bases, Santiago de Chile, pp 487–499

Ajuriaguerra J (1976) Manual de psiquiatría infantil. Barcelona, Toray-Masson

Alatas B, Akin E (2006) An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Comput Fusion Found Methodol Appl 10(3):230–237

Alcala-Fdez J, Fernandez A, Luego J, Derrac J, Garcia S, Sanchez L, Herrera F (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple-Valued Log Soft Comput 17(2–3):255–287

Alcala-Fdez J, Flugy-Pape N, Bonarini A, Herrera F (2010) Analysis of the effectiveness of the genetic algorithms based on extraction of association rules. Fundamenta Informaticae 98(1):1–14MathSciNet

Baudrit C, Dubois D, Perror N (2008) Representing parametric probabilistic models tainted with imprecision. Fuzzy Sets Syst 15(1):1913–1928CrossRef

Bertoluzza C, Gil M, Ralescu D (2003) Statistical modeling. Analysis and management of fuzzy data. Springer, Berlin

Chen C, Hong T, Tseng V (2011) Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering. Soft Comput Fusion Found Methodol Appl. doi:10.1007/s00500-010-0664-1

Couso I, Sanchez L (2008) Higher order models for fuzzy random variables. Fuzzy Sets Syst 159:237–258MathSciNetMATHCrossRef

Delgado M, Marín N, Sánchez D, Vila M (2003) Fuzzy association rules: general model and applications. IEEE Trans Fuzzy Syst 11(2):214–225CrossRef

Dubois D, Hullermeier E, Prade H (2006) A systematic approach to the assessment of fuzzy association rules. Data Min Knowl Disc 13(2):167–192MathSciNetCrossRef

Dubois D, Prade H (1992) When upper probabilities are possibility measures. Fuzzy Sets Syst 49:65–74MathSciNetMATHCrossRef

Dubois D, Prade H, Sudamp T (2005) On the representation, measurement, and discovery of fuzzy associations. IEEE Trans Fuzzy Syst 13(2):250–262CrossRef

Han J, Kamber M (2006) Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann, San FransiscoMATH

Han J, Pei J, Yin Y (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Dis 8(1):53–87MathSciNetCrossRef

Hong T, Kuo C, Chi S (1999) Mining association rules from quantitative data. Intell Data Anal 3(5):363–376MATHCrossRef

Hong T, Kuo C, Chi S (2001) Trade-off between time complexity and number of rules for fuzzy mining from quantitative data. Int J Uncertain Fuzziness Knowl Based Syst 9(5):587–604MATH

Hong T, Lee Y (2008) An overview of mining fuzzy association rules. In: Bustince H, Herrera F, Montero J (eds) Studies in fuzziness and soft computing, vol 220. Springer, Berlin, pp 397–410

Hullermeier E, Yi Y (2007) In defense of fuzzy association analysis. IEEE Trans Syst Man Cybern Part B Cybern 37(4):1039–1043CrossRef

Kaufmann A, Gupta M (1991) Introduction to fuzzy arithmetic: theory and applications. Van Nostrand Reinhold, New YorkMATH

Kaya M (2006) Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules. Soft Comput Fusion Found Methodol Appl 10(7):578–586MathSciNetMATH

Limbourg P (2005) Multi-objective optimization of problems with epistemic uncertainty. In Proceedings of EMO, pp 413–427

Mladenic D, Lavrac N, Bohanec M, Moyle S (2002) Data mining and decision support: integration and collaboration. Kluwer, Norwell

Palacios A, Sanchez L, Couso I (2011) Future performance modelling in athletism with low quality data-based GFSs. J Multiple-Valued Log Soft Comput 17(2–3):207–228

Ruspini E (1969) A new approach to clustering. Inf Control 15:22–32MATHCrossRef

Sanchez L, Couso I, Casillas J (2007) Modelling vague data with genetic fuzzy systems under a combination of crisp and imprecise criteria. In: IEEE symposium on computational intelligence inmulticriteria decision making, pp 30–37

Sanchez L, Couso I, Casillas J (2009) Genetic learning of fuzzy rules on low quality data. Fuzzy Sets Syst 160(17):2524–2552MathSciNetMATHCrossRef

Sanchez L, Suarez M, Villar J, Couso I (2008) Mutual information-based feature selection and partition design in fuzzy rule-based classifiers from vague data. Int J Approx Reason 49:607–622CrossRef

Sudkamp T (2005) Examples, counterexamples, and measuring fuzzy associations. Fuzzy Sets Syst 149(1):57–71MathSciNetMATHCrossRef

Sun K, Fengshan B (2008) Mining weighted association rules without preassigned weights. IEEE Trans Knowl Data Eng 20(4):489–495CrossRef

Thomson P, Gilchrist P (1996) Dyslexia: a multidisciplinary approach. Chapman and Hall, London

Toro J, Cervera M (1980) TALE Test de Análisis de la lectoescritura. Pablo del Río, Madrid

Villar J, Otero A, Otero J, Sanchez L (2009) Taximeter verification using imprecise data from gps and multiobjective algorithms. Eng Appl Artif Intell 22:250–260CrossRef

Vinuessa M, Coll J (1984) Tratado de atletismo. Servicio Geográfico del Ejército

Wu B, Sun C (2001) Interval-valued statistics, fuzzy logic, and their use in computational semantics. J Intell Fuzzy Syst 1–2(11):1–7

Zhang C, Zhang S (2002) Association rule mining: models and algorithms. Springer, BerlinMATHCrossRef

Titel: Mining fuzzy association rules from low-quality data
verfasst von: A. M. Palacios
M. J. Gacto
J. Alcalá-Fdez
Publikationsdatum: 01.05.2012
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 5/2012
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-011-0775-3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 5/2012

Guest editorial: special issue on “knowledge extraction from low quality data: theoretical, methodological and practical issues”

A K-nearest neighbours method based on imprecise probabilities

Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: application to railway track circuit diagnosis

A fuzzy regression model based on distances and random variables with crisp input and fuzzy output data: a case study in biomass production

A fuzzy interval analysis approach to kriging with ill-known variogram and data

Extending information processing in a Fuzzy Random Forest ensemble