• Salvador GarcíaEmail author
  • Julián Luengo
  • Francisco Herrera
Part of the Intelligent Systems Reference Library book series (ISRL, volume 72)


The main background addressed in this book should be presented regarding Data Mining and Knowledge Discovery. Major concepts used throughout the contents of the rest of the book will be introduced, such as learning models, strategies and paradigms, etc. Thus, the whole process known as Knowledge Discovery in Data is provided in Sect. 1.1. A review on the main models of Data Mining is given in Sect. 1.2, accompanied a clear differentiation between Supervised and Unsupervised learning (Sects. 1.3 and 1.4, respectively). In Sect. 1.5, apart from the two classical data mining tasks, we mention other related problems that assume more complexity or hybridizations with respect to the classical learning paradigms. Finally, we establish the relationship between Data Preprocessing with Data Mining in Sect. 1.6.


  1. 1.
    Adamo, J.M.: Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms. Springer, New York (2001)CrossRefGoogle Scholar
  2. 2.
    Aggarwal, C., Reddy, C.: Data Clustering: Recent Advances and Applications. Data Mining and Knowledge Discovery Series. Chapman and Hall/CRC, Taylor & Francis Group, Boca Raton (2013)Google Scholar
  3. 3.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)Google Scholar
  4. 4.
    Alpaydin, E.: Introduction to Machine Learning, 2nd edn. MIT Press, Cambridge (2010)zbMATHGoogle Scholar
  5. 5.
    Amores, J.: Multiple instance classification: review, taxonomy and comparative study. Artif. Intell. 201, 81–105 (2013)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Barber, D.: Bayesian Reasoning and Machine Learning. Cambridge University Press, New York (2012)zbMATHGoogle Scholar
  7. 7.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)Google Scholar
  8. 8.
    Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)CrossRefGoogle Scholar
  9. 9.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)CrossRefGoogle Scholar
  10. 10.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P. (eds.): From data mining to knowledge discovery: an overview as chapter. Advances in Knowledge Discovery and Data Mining. American Association for Artificial Intelligence, San Francisco (1996)Google Scholar
  11. 11.
    Friedman, J.H.: Data mining and statistics: What is the connection The Data Administrative Newsletter? (1997)Google Scholar
  12. 12.
    Frunkranz, J., Gamberger, D., Lavrac, N.: Foundations of Rule Learning. Springer, New York (2012)CrossRefGoogle Scholar
  13. 13.
    Gama, J.: Knowledge Discovery from Data Streams, 1st edn. Chapman & Hall/CRC, Taylor and Francis, Boca Raton (2010)CrossRefzbMATHGoogle Scholar
  14. 14.
    García, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417–435 (2012)CrossRefGoogle Scholar
  15. 15.
    García, S., Luengo, J., Sáez, J.A., López, V., Herrera, F.: A survey of discretization techniques: taxonomy and empirical analysis in supervised learning. IEEE Trans. Knowl. Data Eng. 25(4), 734–750 (2013)CrossRefGoogle Scholar
  16. 16.
    Han, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2011)Google Scholar
  17. 17.
    Herrera, F., Carmona, C.J., González, P., del Jesus, M.J.: An overview on subgroup discovery: foundations and applications. Knowl. Inf. Syst. 29(3), 495–525 (2011)CrossRefGoogle Scholar
  18. 18.
    Liu, H., Motoda, H.: Feature Extraction, Construction and Selection: A Data Mining Perspective. Kluwer International Series in Engineering and Computer Science. Kluwer Academic, Boston (1998)CrossRefzbMATHGoogle Scholar
  19. 19.
    Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer International Series in Engineering and Computer Science. Kluwer Academic, Boston (1998)CrossRefzbMATHGoogle Scholar
  20. 20.
    Liu, H., Motoda, H.: Instance Selection and Construction for Data Mining. Kluwer Academic, Norwell (2001)CrossRefGoogle Scholar
  21. 21.
    Liu, H., Motoda, H.: Computational Methods of Feature Selection. Data Mining and Knowledge Discovery Series. Chapman & Hall/CRC, Taylor and Francis, Boca Raton (2007)zbMATHGoogle Scholar
  22. 22.
    López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250, 113–141 (2013)CrossRefGoogle Scholar
  23. 23.
    Luengo, J., García, S., Herrera, F.: On the choice of the best imputation methods for missing values considering three groups of classification methods. Knowl. Inf. Syst. 32(1), 77–108 (2012)CrossRefGoogle Scholar
  24. 24.
    Nisbet, R., Elder, J., Miner, G.: Handbook of Statistical Analysis and Data Mining Applications. Academic Press, Boston (2009)zbMATHGoogle Scholar
  25. 25.
    Ong, K.: Frequent Pattern Mining. VDM Publishing, Germany (2010)Google Scholar
  26. 26.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  27. 27.
    Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann, San Francisco (1999)Google Scholar
  28. 28.
    Rokach, L.: Data Mining with Decision Trees: Theory and Applications. Series in Machine Perception and Artificial Intelligence. World Scientific, Singapore (2007)Google Scholar
  29. 29.
    Sáez, J.A., Luengo, J., Herrera, F.: Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recogn. 46(1), 355–364 (2013)CrossRefGoogle Scholar
  30. 30.
    Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Adaptive Computation and Machine Learning. MIT Press, Cambridge (2002)Google Scholar
  31. 31.
    Triguero, I., Derrac, J., García, S., Herrera, F.: A taxonomy and experimental study on prototype generation for nearest neighbor classification. IEEE Trans. Syst. Man Cybern. Part C 42(1), 86–100 (2012)CrossRefGoogle Scholar
  32. 32.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems, 2nd edn. Morgan Kaufmann, San Francisco (2005)Google Scholar
  33. 33.
    Zhu, X., Goldberg, A.B., Brachman, R.: Introduction to Semi-Supervised Learning. Morgan and Claypool, California (2009)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Salvador García
    • 1
    Email author
  • Julián Luengo
    • 2
  • Francisco Herrera
    • 3
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  3. 3.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

Personalised recommendations