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Knowledge Discovery in Sport

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Computational Intelligence in Sports

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 22))

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Abstract

The chapter deals with knowledge discovery from data in sport. In the narrower sense, knowledge discovery from data refers to a data mining that also incorporates methods from other domains, like statistics, pattern recognition, machine learning, visualization, association rule mining and computational intelligence algorithms.

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References

  1. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, New York, NY (2011)

    MATH  Google Scholar 

  2. Breckner, D., Abel, P.: Principles of Business Computer Programming. Prentice Hall, Upper Saddle River, NJ (1971)

    Google Scholar 

  3. Hrovat, G., Fister Jr., I., Yermak, K., Stiglic, G., Fister, I.: Interestingness measure for mining sequential patterns in sports. J. Intell. Fuzzy Syst. 29(5), 1981–1994 (2015)

    Article  Google Scholar 

  4. Baca, A., Dabnichki, P., Heller, M., Kornfeind, P.: Ubiquitous computing in sports: a review and analysis. J. Sports Sci. 27(12), 1335–1346 (2009). https://doi.org/10.1080/02640410903277427

    Article  Google Scholar 

  5. Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., Havinga, P.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: Proceedings of the 23th International Conference on Architecture of Computing Systems, ARCS 2010, pp. 167–176. VDE Verlag, Berlin (2010)

    Google Scholar 

  6. Chi, E.H.: Sensors and ubiquitous computing technologies in sports. WIT Trans. State Art Sci. Eng. 32, 249–268 (2008). https://doi.org/10.2495/978-1-84564-064-4/09

    Article  Google Scholar 

  7. Knudson, D.: Future trends in the kinesiology sciences. Quest 68(3), 348–360 (2016). https://doi.org/10.1080/00336297.2016.1184171

    Article  Google Scholar 

  8. Hrovat, G., Stiglic, G., Kokol, P., Ojsteršek, M.: Contrasting temporal trend discovery for large healthcare databases. Comput. Methods Programs Biomed. 113(1), 251–257 (2014). https://doi.org/10.1016/j.cmpb.2013.09.005

    Article  Google Scholar 

  9. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22, 207–216 (1993)

    Article  Google Scholar 

  10. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1994)

    Google Scholar 

  11. Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000). https://doi.org/10.1109/69.846291

    Article  Google Scholar 

  12. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2), 1–12 (2000). https://doi.org/10.1145/335191.335372

    Article  Google Scholar 

  13. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York, NY, USA (1979)

    MATH  Google Scholar 

  14. Claparede, E., Stern, W.: La psyschologie de l’intelligence. Scientia 22, 253–268 (1917)

    Google Scholar 

  15. Büchler, K.: Die Kriese der Psychologie. Fischer, Jena (1927)

    Google Scholar 

  16. Piaget, J.: The Psychology of Intelligence. Routledge Classics, New York, NY (2001)

    Google Scholar 

  17. Russel, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, New Jersey (2009)

    Google Scholar 

  18. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin (2003)

    Book  MATH  Google Scholar 

  19. Blum, C., Merkle, D.: Swarm Intelligence: Introduction and Applications. Springer, Berlin (2008)

    Book  MATH  Google Scholar 

  20. Zadeh, L.A.: Fuzzy algorithms. Inf. Control 12(2), 94–102 (1968). https://doi.org/10.1016/S0019-9958(68)90211-8

    Article  MathSciNet  MATH  Google Scholar 

  21. Dasgupta, D.: Information processing in the immune system. In: Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., Price, K.V. (eds.) New Ideas in Optimization, 3rd edn. McGraw-Hill Ltd., UK, Maidenhead, UK, England (1999)

    Google Scholar 

  22. Erdos, P.: Graph theory and probability. Can. J. Math. 11, 34–38 (1959). https://doi.org/10.4153/CJM-1959-003-9

    Article  MathSciNet  MATH  Google Scholar 

  23. Engelbrecht, A.P.: Computational Intelligence: An Introduction, 2nd edn. Wiley Publishing (2007)

    Google Scholar 

  24. Darwin, C.: On the Origin of Species. Harvard University Press, London (1852)

    Google Scholar 

  25. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc, Boston, MA, USA (1989)

    MATH  Google Scholar 

  26. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge, MA, USA (1994)

    MATH  Google Scholar 

  27. Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming. Genetic Algorithms. Oxford University Press, Oxford, UK (1996)

    MATH  Google Scholar 

  28. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Willey, New York, US (1966)

    MATH  Google Scholar 

  29. Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997). https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  30. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  31. Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010). https://doi.org/10.1007/s10462-009-9137-2

    Article  Google Scholar 

  32. Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  33. Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Proceed NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy (1989)

    Google Scholar 

  34. Kennedy, J., Eberhart, R.C.: The particle swarm optimization; social adaptation in information processing. In: Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., Price, K.V. (eds.) New Ideas in Optimization, 3rd edn. McGraw-Hill Ltd., UK, Maidenhead, UK, England (1999)

    Google Scholar 

  35. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12538-6_6

    Chapter  Google Scholar 

  36. Yang, X.S., Deb, S.: Cuckoo search via levy flights. In: World Congress & Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE Publication (2009)

    Google Scholar 

  37. Yang, X.S.: Firefly algorithm. In: Nature-Inspired Metaheuristic Algorithms, pp. 79–90. Luniver Press, London, UK (2008)

    Google Scholar 

  38. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J. Glob. Optim. 39(3), 459–471 (2007). https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  39. Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., Price, K.V. (eds.) New Ideas in Optimization, pp. 11–32. McGraw-Hill Ltd., UK, Maidenhead, UK, England (1999)

    Google Scholar 

  40. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983). https://doi.org/10.1126/science.220.4598.671

    Article  MathSciNet  MATH  Google Scholar 

  41. Fister Jr., I., Fister, D., Yang, X.S.: A hybrid bat algorithm. Electrotech. Rev. 80(1–2), 1–7 (2013)

    MATH  Google Scholar 

  42. Shlens, J.: A tutorial on principal component analysis. In: Systems Neurobiology Laboratory, Salk Institute for Biological Studies (2005)

    Google Scholar 

  43. Rauter, S., Fister Jr., I., Fister, I.: A collection of sport activity files for data analysis and data mining (2015). Technical Report

    Google Scholar 

  44. Rauter, S., Fister Jr., I., Fister, I.: A collection of sport activity files for data analysis and data mining 2016a (2016). Technical Report

    Google Scholar 

  45. Fister Jr., I., Rauter, S., Fister, D., Fister, I.: A collection of sport activity datasets for data analysis and data mining 2016b (2016). Technical Report

    Google Scholar 

  46. Fister Jr., I., Rauter, S., Fister, D., Fister, I.: A collection of sport activity datasets with an emphasis on powermeter data (2017). Technical Report

    Google Scholar 

  47. Perl, J., Baca, A.: Application of neural networks to analyze performance in sports. In: Proceedings of the 8th Annual Congress of the European College of Sport Science, Salzburg: ECSS, vol. 342 (2003)

    Google Scholar 

  48. Brown, N., Bichler, S., Fiedler, M., Alt, W.: Fatigue detection in strength training using three-dimensional accelerometry and principal component analysis. Sports Biomech. 15(2), 139–150 (2016)

    Article  Google Scholar 

  49. Deak, G.F., Miron, R., Avram, C.C., Adina, A., et al.: Fuzzy based analysis method of high-density surface electromyography maps for physical training assessment. In: 2016 IEEE International Conference on : Automation, Quality and Testing, Robotics (AQTR), pp. 1–6. IEEE (2016)

    Google Scholar 

  50. Chen, H.: Building a basketball shooting model based on neural networks and a genetic algorithm. World Trans. Eng. Technol. Educ. 11(3), 310–315 (2013)

    Google Scholar 

  51. Me, E., Unold, O., et al.: Machine learning approach to model sport training. Comput. Hum. Behav. 27(5), 1499–1506 (2011)

    Article  Google Scholar 

  52. Balague, N., Torrents, C., Hristovski, R., Davids, K., Araújo, D.: Overview of complex systems in sport. J. Syst. Sci. Complex. 26(1), 4–13 (2013)

    Article  MATH  Google Scholar 

  53. Brzostowski, K., Drapała, J., Grzech, A., Światek, P.: Adaptive decision support system for automatic physical effort plan generation-data-driven approach. Cybern. Syst. 44(2–3), 204–221 (2013)

    Article  Google Scholar 

  54. Henriet, J.: Artificial intelligence-virtual trainer: an educative system based on artificial intelligence and designed to produce varied and consistent training lessons. Proc. Inst. Mech. Eng. Part P J. Sports Eng. Technol. 111–132 (2016)

    Google Scholar 

  55. Baca, A.: Adaptive systems in sports. Social Networks and the Economics of Sports, pp. 115–124. Springer (2014)

    Google Scholar 

  56. Wiktorowicz, K., Przednowek, K., Lassota, L., Krzeszowski, T.: Predictive modeling in race walking. Comput. Intell. Neurosci. 2015, 10 (2015)

    Article  Google Scholar 

  57. Fister Jr., I., Ljubič, K., Suganthan, P.N., Perc, M., Fister, I.: Computational intelligence in sports: challenges and opportunities within a new research domain. Appl. Math. Comput. 262, 178–186 (2015)

    MathSciNet  MATH  Google Scholar 

  58. Fister, I., Rauter, S., Yang, X.S., Ljubič Fister, K., Fister Jr., I.: Planning the sports training sessions with the bat algorithm. Neurocomputing 149, 993–1002 (2015)

    Article  Google Scholar 

  59. Hrovat, G., Fister Jr., I., Yermak, K., Štiglic, G., Fister, I.: Interestingness measure for mining sequential patterns in sports. J. Intell. Fuzzy Syst. 29(5), 1981–1994 (2015)

    Article  Google Scholar 

  60. Fister Jr., I., Rauter, S., Ljubič Fister, K., Fister, D., Fister, I.: Planning fitness training sessions using the bat algorithm. In: 15th Conference on ITAT 2015 (CEUR Workshop Proceedings), vol. 1422, pp. 121–126 (2015). ISSN 1613-0073

    Google Scholar 

  61. Fister Jr., I., Fister, I., Fister, D., Ljubič, K., Zhuang, Y., Fong, S.: Towards automatic food prediction during endurance sport competitions. In: 2014 International Conference on Soft Computing and Machine Intelligence (ISCMI), pp. 6–10. IEEE (2014)

    Google Scholar 

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Fister, I., Fister Jr., I., Fister, D. (2019). Knowledge Discovery in Sport. In: Computational Intelligence in Sports. Adaptation, Learning, and Optimization, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-03490-0_2

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