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Mining fuzzy association rules from low-quality data

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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.

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References

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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):1–14

    MathSciNet  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  • 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 159:237–258

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  Google Scholar 

  • 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):587–604

    MATH  Google Scholar 

  • 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

    Google Scholar 

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

    Article  Google Scholar 

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

    MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

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

    Article  MATH  Google Scholar 

  • 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):2524–2552

    Article  MathSciNet  MATH  Google Scholar 

  • 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 49:607–622

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  • 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 22:250–260

    Article  Google Scholar 

  • 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

    Google Scholar 

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

    Book  MATH  Google Scholar 

Download references

Acknowledgments

This study was supported by the Spanish Ministry of Education and Science under Grants no. TIN2008-06681-C06-{01 and 04}, TIN2011-28488 and by the Principado de Asturias under Grant PCTI 2006–2009.

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Authors and Affiliations

  1. Department of Computer Science, University of Oviedo, 33204, Gijón, Spain

    A. M. Palacios

  2. Department of Computer Science, University of Jaén, 23071, Jaén, Spain

    M. J. Gacto

  3. Department of Computer Science and Artificial Intelligence, CITIC-UGR, University of Granada, 18071, Granada, Spain

    J. Alcalá-Fdez

Authors
  1. A. M. Palacios
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  2. M. J. Gacto
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  3. J. Alcalá-Fdez
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Correspondence to A. M. Palacios.

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Palacios, A.M., Gacto, M.J. & Alcalá-Fdez, J. Mining fuzzy association rules from low-quality data. Soft Comput 16, 883–901 (2012). https://doi.org/10.1007/s00500-011-0775-3

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