Abstract
Association Rule Mining belongs to one of the more prominent methods in Data Mining, where relations are looked for among features in a transaction database. Normally, algorithms for Association Rule Mining mine a lot of association rules, from which it is hard to extract knowledge. This paper proposes a new visualization method capable of extracting information hidden in a collection of association rules using numerical attributes, and presenting them in the form inspired by prominent cycling races (i.e., the Tour de France). Similar as in the Tour de France cycling race, where the hill climbers have more chances to win the race when the race contains more hills to overcome, the virtual hill slopes, reflecting a probability of one attribute to be more interesting than the other, help a user to understand the relationships among attributes in a selected association rule. The visualization method was tested on data obtained during the sports training sessions of a professional athlete that were processed by the algorithms for Association Rule Mining using numerical attributes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD 1993, pp. 207–216. ACM, New York (1993). http://doi.acm.org/10.1145/170035.170072
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on VLDB, pp. 487–499 (1994)
Altay, E.V., Alatas, B.: Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining. J. Ambient Intell. Hum. Comput. 11, 1–21 (2019). https://doi.org/10.1007/s12652-019-01540-7
Arrieta, A.B., et al.: Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Inf. Fusion 58, 82–115 (2020)
Fader, P.S., Hardie, B.G.S., Shang, J.: Customer-base analysis in a discrete-time noncontractual setting. Market. Sci. 29(6), 1086–1108 (2010)
Fister Jr., I., Iglesias, A., Galvez, A., Del Ser, J., Osaba, E., Fister, I.: Differential evolution for association rule mining using categorical and numerical attributes. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 79–88 (2018)
Hahsler, M., Karpienko, R.: Visualizing association rules in hierachical groups. J. Bus. Econ. 87, 317–335 (2017)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD 2000, pp. 1–12. Association for Computing Machinery, New York (2000). https://doi.org/10.1145/342009.335372
Lee, T.Y., Bradlow, E.T.: Automated marketing research using online customer reviews. J. Market. Res. 48(5), 881–894 (2011)
Lucía, A., Earnest, C., Arribas, C.: The Tour de France: a physiological review (2003)
Lucía, A., Hoyos, J., Santalla, A., Earnest, C., Chicharro, J.L.: Tour de France versus Vuelta a España: which is harder? Med. Sci. Sports Exerc. 35(5), 872–878 (2003)
Netzer, O., Feldman, R., Goldenberg, J., Fresko, M.: Mine your own business: market-structure surveillance through text mining. Market. Sci. 31(3), 521–543 (2012)
Rogge, N., Reeth, D.V., Puyenbroeck, T.V.: Performance evaluation of tour de france cycling teams using data envelopment analysis. Int. J. Sport Finance 8(3), 236–257 (2013)
Rooderkerk, R.P., Van Heerde, H.J., Bijmolt, T.H.: Optimizing retail assortments. Market. Sci. 32(5), 699–715 (2013)
Sanders, D., Heijboer, M.: Physical demands and power profile of different stage types within a cycling grand tour. Eur. J. Sport Sci. 19(6), 736–744 (2019)
Santalla, A., Earnest, C.P., Marroyo, J.A., Lucía, A.: The Tour de France: an updated physiological review (2012)
Sundhagen, T.A.: Lance Armstrong: an American Legend? (2011)
Torgler, B.: “La Grande Boucle" : determinants of success at the Tour de France. J. Sports Econ. 8(3), 317–331 (2007)
Van Erp, T., Hoozemans, M., Foster, C., De Koning, J.J.: Case report: load, intensity, and performance characteristics in multiple grand tours. Med. Sci. Sports Exerc. 52(4), 868–875 (2020)
Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New Algorithms for Fast Discovery of Association Rules. Technical report, USA (1997)
Acknowledgments
Iztok Fister thanks the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0042 - Digital twin). Iztok Fister Jr. thanks the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0057). Dušan Fister thanks the financial support from the Slovenian Research Agency (Research Core Funding No. P5-0027). J. Del Ser and E. Osaba would like to thank the Basque Government through EMAITEK and ELKARTEK (ref. 3KIA) funding grants. J. Del Ser also acknowledges funding support from the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1294-19). Andres Iglesias and Akemi Galvez acknowledge financial support from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 778035, and the Spanish Ministry of Science, Innovation, and Universities (Computer Science National Program) under grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Funds EFRD (AEI/FEDER, UE).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fister, I. et al. (2020). Visualization of Numerical Association Rules by Hill Slopes. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-62362-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62361-6
Online ISBN: 978-3-030-62362-3
eBook Packages: Computer ScienceComputer Science (R0)