Introduction to Knowledge Discovery and Data Mining

  • Oded Maimon
  • Lior Rokach


Knowledge Discovery in Databases (KDD) is an automatic, exploratory analysis and modeling of large data repositories. KDD is the organized process of identifying valid, novel, useful, and understandable patterns from large and complex data sets. Data Mining (DM) is the core of the KDD process, involving the inferring of algorithms that explore the data, develop the model and discover previously unknown patterns. The model is used for understanding phenomena from the data, analysis and prediction.


Data Mining Knowledge Discovery Swarm Intelligence Iterate Function System Data Mining Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Arbel, R. and Rokach, L., Classifier evaluation under limited resources, Pattern Recognition Letters, 27(14): 1619–1631, 2006, Elsevier.CrossRefGoogle Scholar
  2. Averbuch, M. and Karson, T. and Ben-Ami, B. and Maimon, O. and Rokach, L., Contextsensitive medical information retrieval, The 11th World Congress on Medical Informatics (MEDINFO 2004), San Francisco, CA, September 2004, IOS Press, pp. 282–286.Google Scholar
  3. Cohen S., Rokach L., Maimon O., Decision Tree Instance Space Decomposition with Grouped Gain-Ratio, Information Science, Volume 177, Issue 17, pp. 3592-3612, 2007.CrossRefGoogle Scholar
  4. Hastie, T. and Tibshirani, R. and Friedman, J. and Franklin, J., The elements of statistical learning: data mining, inference and prediction, The Mathematical Intelligencer, 27(2): 83–85, 2005.CrossRefGoogle Scholar
  5. Han, J. and Kamber, M., Data mining: concepts and techniques, Morgan Kaufmann, 2006.Google Scholar
  6. H. Kriege, K. M. Borgwardt, P. Krger, A. Pryakhin, M. Schubert and Arthur Zimek, Future trends in data mining, Data Mining and Knowledge Discovery, 15(1):87-97, 2007.CrossRefMathSciNetGoogle Scholar
  7. Larose, D.T., Discovering knowledge in data: an introduction to data mining, JohnWiley and Sons, 2005.Google Scholar
  8. Maimon O., and Rokach, L. Data Mining by Attribute Decomposition with semiconductors manufacturing case study, in Data Mining for Design and Manufacturing: Methods and Applications, D. Braha (ed.), Kluwer Academic Publishers, pp. 311–336, 2001.Google Scholar
  9. Maimon O. and Rokach L., “Improving supervised learning by feature decomposition”, Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems, Lecture Notes in Computer Science, Springer, pp. 178-196, 2002.Google Scholar
  10. Maimon, O. and Rokach, L., Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications, Series in Machine Perception and Artificial Intelligence - Vol. 61, World Scientific Publishing, ISBN:981-256-079-3, 2005.Google Scholar
  11. Rokach, L., Decomposition methodology for classification tasks: a meta decomposer framework, Pattern Analysis and Applications, 9(2006):257–271.CrossRefMathSciNetGoogle Scholar
  12. Rokach L., Genetic algorithm-based feature set partitioning for classification problems, Pattern Recognition, 41(5):1676–1700, 2008.zbMATHCrossRefGoogle Scholar
  13. Rokach L., Mining manufacturing data using genetic algorithm-based feature set decomposition, Int. J. Intelligent Systems Technologies and Applications, 4(1):57-78, 2008.CrossRefGoogle Scholar
  14. Rokach L., Maimon O. and Lavi I., Space Decomposition In Data Mining: A Clustering Approach, Proceedings of the 14th International Symposium On Methodologies For Intelligent Systems, Maebashi, Japan, Lecture Notes in Computer Science, Springer-Verlag, 2003, pp. 24–31.Google Scholar
  15. Rokach, L. and Maimon, O. and Averbuch, M., Information Retrieval System for Medical Narrative Reports, Lecture Notes in Artificial intelligence 3055, page 217-228 Springer-Verlag, 2004.Google Scholar
  16. Rokach, L. and Maimon, O. and Arbel, R., Selective voting-getting more for less in sensor fusion, International Journal of Pattern Recognition and Artificial Intelligence 20 (3) (2006), pp. 329–350.CrossRefGoogle Scholar
  17. Rokach, L. and Maimon, O., Theory and applications of attribute decomposition, IEEE International Conference on Data Mining, IEEE Computer Society Press, pp. 473–480, 2001.Google Scholar
  18. Rokach L. and Maimon O., Feature Set Decomposition for Decision Trees, Journal of Intelligent Data Analysis, Volume 9, Number 2, 2005b, pp 131–158.Google Scholar
  19. Rokach, L. and Maimon, O., Clustering methods, Data Mining and Knowledge Discovery Handbook, pp. 321–352, 2005, Springer.Google Scholar
  20. Rokach, L. and Maimon, O., Data mining for improving the quality of manufacturing: a feature set decomposition approach, Journal of Intelligent Manufacturing, 17(3):285–299, 2006, Springer.CrossRefGoogle Scholar
  21. Rokach, L., Maimon, O., Data Mining with Decision Trees: Theory and Applications,World Scientific Publishing, 2008.Google Scholar
  22. Witten, I.H. and Frank, E., Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann Pub, 2005.Google Scholar
  23. Wu, X. and Kumar, V. and Ross Quinlan, J. and Ghosh, J. and Yang, Q. and Motoda, H. and McLachlan, G.J. and Ng, A. and Liu, B. and Yu, P.S. and others, Top 10 algorithms in data mining, Knowledge and Information Systems, 14(1): 1–37, 2008.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Industrial EngineeringTel-Aviv UniversityRamat-AvivIsrael
  2. 2.Department of Information System EngineeringBen-Gurion UniversityBeer-ShebaIsrael

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