Encyclopedia of Complexity and Systems Science

2009 Edition
| Editors: Robert A. Meyers (Editor-in-Chief)

Discovery Systems

  • Petra Povalej
  • Mateja Verlic
  • Gregor Stiglic
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30440-3_125

Definition of the Subject

By definition, to discover is to see, get knowledge of, learn of, find or find out; gain sight or knowledge ofsomething previously unseen or unknown [18], therefore a discovery system can be defined asa system that supports the process of finding new knowledge. Results of a simple query for discoverysystem on the World Wide Web returns different types of discovery systems: from knowledge discovery systems in databases, internet‐basedknowledge discovery, service discovery systems and resource discovery systems to more specific, like for example drug discovery systems [10], gene discovery systems [43], discovery system forpersonality profiling [48], and developmental discovery systems [17] among others. As illustrated variety of discovery systems can be found in many different research areas, but wewill focus on knowledge discovery and knowledge discovery systems from the computer science perspective. Inconsistent definitions of terms knowledgediscovery...

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Bibliography

Primary Literature

  1. 1.
    Anand S, Buchner A (1998) Decision support using data mining. Financial Time Management, LondonGoogle Scholar
  2. 2.
    Baeck T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, New YorkzbMATHGoogle Scholar
  3. 3.
    Barley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159Google Scholar
  4. 4.
    Becerra‐Fernandez I, Gonzalez A, Sabherwal R (2004) Knowledge management: Challenges, solutions, and technologies. Prentice Hall, Upper Saddle RiverGoogle Scholar
  5. 5.
    Beck JR, Shultz E (1986) The use of relative operating characteristic (ROC) curves in test performance evaluation. Arch Pathol Lab Med 110:13–20Google Scholar
  6. 6.
    Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MathSciNetzbMATHGoogle Scholar
  7. 7.
    Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, BelmontzbMATHGoogle Scholar
  8. 8.
    Boz O (2000) Converting a trained neural network to a decision tree dectext – decision tree extractor. Ph D thesis, Computer Science and Engineering, Lehigh University. http://citeseer.ist.psu.edu/boz00converting.html. Accessed 12 Nov 2007
  9. 9.
    Cabena P, Hadjinian P, Stadler R, Verhees J, Zanasi A (1998) Discovering data mining: From concepts to implementation. Prentice Hall, Upper Saddle RiverGoogle Scholar
  10. 10.
    Caspase Drug Discovery Systems. drug discovery system. http://www.biomol.com/Online_Catalog/Online_Catalog/Products/36/?categoryId=420. Accessed 6 Nov 2007
  11. 11.
    Cios K, Teresinska A, Konieczna S, Potocka J, Sharma S (2000) Diagnosing myocardial perfusion from PECT bull's‐eye maps – a knowledge discovery approach. IEEE Eng Med Biol Mag, Special Issue Med Data Mining Knowl Discov 19(4):17–25Google Scholar
  12. 12.
    Cios KJ, Pedrycz W, Swiniarski RW, Kurgan LA (2007) Data mining. A knowledge discovery approach. Springer, New YorkzbMATHGoogle Scholar
  13. 13.
    Dalgaard P (2002) Introductory statistics with R. Springer, New YorkzbMATHGoogle Scholar
  14. 14.
    Davenport TH, Prusak L (1997) Information ecology: Mastering the information and knowledge environment. Oxford University Press, New YorkGoogle Scholar
  15. 15.
    Dennis JE Jr, Schnabel RB (1989) A view of unconstrained optimization. In: Nemhauser GL, Runnooy Kan AHG, Todd MJ (eds) Handbook in operations research and management science, vol 1 Optimization. Elsevier, AmsterdamGoogle Scholar
  16. 16.
    Demsar J, Zupan B (2004) Orange: From experimental machine learning to interactive data mining. White Paper. Faculty of Computer and Information Science, University of Ljubljana. http://www.ailab.si/orange
  17. 17.
    Developmental Discovery System (TM). Developmental discovery system. http://www.gotofocus.com/. Accessed 6 Nov 2007
  18. 18.
    Dictionary.com Unabridged (v 1.1). discover. http://dictionary.reference.com/browse/discover. Accessed 5 Nov 2007
  19. 19.
    Dietterich TG (2000) Ensemble methods in machine learning. In: First International Workshop on Multiple Classifier Systems. Lecture Notes in Computer Science. Springer, New York, pp 1–15Google Scholar
  20. 20.
    Dixon J (2005) Pentaho Open Source Business Intelligence Platform Technical White Paper. Pentaho Corporation, Orlando. http://sourceforge.net/project/showfiles.php?group_id=140317
  21. 21.
    Fayyad U, Piatetsky‐Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases (a survey). AI Mag 17(3):37–54Google Scholar
  22. 22.
    Fayyad U, Piatesky‐Shapiro G, Smyth P, Uthurusamy R (eds) (1996) Advances in knowledge discovery and data mining. AAAI Press, Menlo ParkGoogle Scholar
  23. 23.
    Frawley W, Piatesky‐Shapiro G, Matheus C (1991) Knowledge discovery in databases: An overview. In: Piatesky‐Shapiro G, Frowley W (eds) Knowledge Discovery in Databases. AAAI/MIT Press, pp 1–27, Menlo ParkGoogle Scholar
  24. 24.
    Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings Thirteenth International Conference on Machine Learning. Morgan Kaufman, San Francisco, pp 148–156Google Scholar
  25. 25.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison, ReadingzbMATHGoogle Scholar
  26. 26.
    Hand D, Mannila H, Smyth P (eds) (2001) Principles of data mining. MIT Press, CambridgeGoogle Scholar
  27. 27.
    Holland JH (1975) Adaptation in natural and artificial systems. MIT Press, CambridgeGoogle Scholar
  28. 28.
    Iglesias CJ (1996) The role of hybrid systems in intelligent data management: The case of fuzzy/neural hybrids. Control Eng Pract 4(6):839–845MathSciNetGoogle Scholar
  29. 29.
    Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. Appl Stat 29:119–127ADSGoogle Scholar
  30. 30.
    Kurgan L, Musilek P (2006) A survey of Knowledge Discovery and Data Mining process models. Knowl Eng Rev 21(1):1–24Google Scholar
  31. 31.
    Loh W, Shih Y (1997) Split selection methods for classification trees. Stat Sinica 7:815–840MathSciNetzbMATHGoogle Scholar
  32. 32.
    Mannila H (2000) Theoretical frameworks of data mining. SIGKDD Explor 1:30–32Google Scholar
  33. 33.
    Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T (2006) YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proc of the 12th ACMSIGKDD. International Conference on Knowledge Discovery and Data Mining, Philadelphia, pp 1–6 Google Scholar
  34. 34.
    Pechenizkiy M, Tsymbal A, Puuronen S (2005) Meta‐knowledge management in multistrategy process‐oriented knowledge discovery systems. Technical Report, Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2005–30, p 12Google Scholar
  35. 35.
    Piatetsky‐Shapiro G (1991) Knowledge discovery in real databases: A report on the IJCAI-89 Workshop. AI Mag 11(5):68–70Google Scholar
  36. 36.
    Piatetsky‐Shapiro G (1999) The data mining industry coming to age. IEEE Intel Syst 14(6):32–33Google Scholar
  37. 37.
    Provost F, Fawcett T, Kohavi R (1998) The case against accuracy estimation for comparing classifiers. In: Proceedings of the Fifteenth International Conference on Machine Learning, (ICML-98), San FranciscoGoogle Scholar
  38. 38.
    Quinlan JR (1986) Induction of decision trees. In: Machine Learning, vol 1. Kluwer, HinghamGoogle Scholar
  39. 39.
    Quinlan R (1993) C4.5: Programs for machine learning. Morgan Kaufmann, San FranciscoGoogle Scholar
  40. 40.
    Rakotomalala R (2005) TANAGRA: Un logiciel gratuit pour l'enseignement et la recherche. In: Proc of the 5th Journees d'Extraction et Gestion des Connaissances 2:697–702Google Scholar
  41. 41.
    Reeves CR (ed) (1993) Modern heuristic techniques for combinatorial problems. Wiley, New YorkzbMATHGoogle Scholar
  42. 42.
    Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back‐propagating errors. Nature 323:533–536ADSGoogle Scholar
  43. 43.
    Sano M, Katoa Y, Taira K (2005) Functional gene‐discovery systems based on libraries of hammerhead and hairpin ribozymes and short hairpin RNAs. Mol Biosyst 1:27–35Google Scholar
  44. 44.
    Shearer C (2000) The CRISP-DM model: the new blueprint for data mining. J Data Wareh l5(4):13–19Google Scholar
  45. 45.
    Smyth P, Goodman RM (1991) Rule induction using information theory. In: Piatetsky‐Schapiro G, Frawley WJ (eds) Knowledge Discovery in Databases. AAAI Press, pp 159–176, Menlo ParkGoogle Scholar
  46. 46.
    Snedecor GW, Cochran WG (1989) Statistical methods, 8th edn. Iowa State University Press, AmeszbMATHGoogle Scholar
  47. 47.
    Tan P, Steinbach M, Kumar V (2005) Introduction to data mining. Addison, BostonGoogle Scholar
  48. 48.
    The Discovery System. discovery system for personality profiling. http://www.insights.com/core/English/TheDiscoverySystem/default.shtm. Accessed 6 Nov 2007
  49. 49.
    Towsey M, Alpsan D, Sztriha L (1995) Training a neural network with conjugate gradient methods. IEEE Proc Neural Netw 1:373–378Google Scholar
  50. 50.
    Weiss GM, Provost F (2001) The effect of class distribution on classifier learning. Technical Report ML-TR 43, Department of Computer Science, Rutgers UniversityGoogle Scholar
  51. 51.
    Werbos PJ (1994) The roots of backpropagation. Wiley, New YorkGoogle Scholar
  52. 52.
    Witten IH, Frank E (2005) Data mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San FranciscoGoogle Scholar
  53. 53.
    Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82Google Scholar

Books and Reviews

  1. 54.
    Berthold M, Hand DJ (2003) Intelligent data analysis: An introduction, 2nd edn. Springer, New YorkGoogle Scholar
  2. 55.
    Lin TY, Ohsuga S, Liau CJ, Hu X, Tsumoto S (eds) (2005) Foundations of data mining and knowledge discovery. Studies in Computational Intelligence, vol 6. Springer, New YorkGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Petra Povalej
    • 1
  • Mateja Verlic
    • 1
  • Gregor Stiglic
    • 1
  1. 1.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia