A Logical Clock Based Discovery of Patterns

Friedemann Schwenkreis

doi:doi:10.11648/j.ijdsa.20210704.11

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A Logical Clock Based Discovery of Patterns

Friedemann Schwenkreis

Published in International Journal of Data Science and Analysis (Volume 7, Issue 4)

Received: 29 July 2021 Accepted: 7 August 2021 Published: 11 August 2021

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Abstract

This paper focusses on aspects of applied data mining in the context of team handball. It presents an approach to transform the collected data of team handball matches into formats that allow the use of classification and methods to search for association rules. To be able to search for patterns at arbitrary times of matches a concept of a logical clock is introduced, which becomes an essential part of the data preparation. The applied data mining methods are described in detail using RapidMiner processes and their settings. However, the approach is independent of the used data mining tool. Based on the results of the data mining processes, the applicability of data mining techniques in the given context will be discussed. Particularly it will be shown that rule-based results have significant advantages compared to approaches using support vector machines in the given context. The results are also compared based on the logical clock which will show how patterns evolve over time in case of team handball. We will show that the overall prediction accuracy of a model is not the primary concern in the chosen application area. It is rather to discover rules which clearly help to identify the need for action. The concept of time is crucial in this context because rules are less helpful if they are detected when the game is over, and we are at the end of a slippery slope which could have been prevented long before.

Published in	International Journal of Data Science and Analysis (Volume 7, Issue 4)
DOI	10.11648/j.ijdsa.20210704.11
Page(s)	98-108
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Data Science, Applied Data Mining, Classification, Co-Occurrence Grouping, Team Handball

References

[1]	R. P. Schumaker, O. K. Solieman und H. Chen, Sports Data Mining, Springer, 2010.
[2]	U. Brefeld, J. Davis, J. Van Haaren und A. Zimmermann, Hrsg., Machine Learning and Data Mining for Sports Analytics, Springer, 2020.
[3]	F. Goes, L. Meerhoff, M. Bueno, D. Rodrigues, F. Moura, M. Brink, M. Elferink-Gemser, A. Knobbe, S. Cunha, R. Torres und K. Lemmink, “Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review,” European Journal of Sport Science, pp. 481-496, 21: 4, 2021.
[4]	J. Lee, J. Lee, S. Moon, D. Nam und W. Yoo, “Basketball event recognition technique using Deterministic Finite Automata (DFA),” in Proceedings of the 20th International Conference on Advanced Communication Technology (ICACT), Chuncheon, Korea (South), 2018.
[5]	F. Schwenkreis, “A Three Component Approach To Support Team Handball Coaches”, in 23rd Annual Congress of the European College of Sport Science, Dublin, 2018.
[6]	R. Agrawal und R. Srikant, “Fast Algorithms for Mining Association Rules”, in Proceedings of the 20th VLDB Conference, Santiago Chile, 1994.
[7]	M. Hashler, B. Grün und K. Hornik, “arules - A Computational environment for Mining Association Rules and Frequent Item Sets”, Journal of Statistical Software, Bd. 14, Nr. 15, pp. 1-25, October 2005.
[8]	RapidMiner, “RapidMiner Studio”, 2021. [Online]. Available: https://rapidminer.com/products/studio/. [Zugriff am April 2021].
[9]	F. Schwenkreis, “Why the Concept of Shopping Baskets helps to analyze Team-Handball”, in Proceedings of the 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), Valencia, Spain, 2020.
[10]	F. Schwenkreis und E. Nothdurft, “Applied Data Science: An Approach to Explain a Complex Team Ball Game”, in Proceedings of the 9th International Conference on Data Science, Technology and Applications, DATA 2020, Lieusaint, Paris, 2020.
[11]	Sportradar, Handball Scout Admin (HAS) Manual, Sportradar AG, 2015.
[12]	F. Provost und T. Fawcett, Data Science for Business, Sebastopol, CA: O'Reilly and Associates, 2013.
[13]	J. Trost, “Statistically nonrepresentative stratified sampling: A sampling technique for qualitative studies”, Qual Sociol, pp. 54-57, March 1986.
[14]	I. Steinwart und A. Christmann, Support Vector Machines, New York City: Springer, 2008.
[15]	T. K. Ho, “Random Decision Forests”, in Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, 1995.

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Friedemann Schwenkreis. (2021). A Logical Clock Based Discovery of Patterns. International Journal of Data Science and Analysis, 7(4), 98-108. https://doi.org/10.11648/j.ijdsa.20210704.11

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Friedemann Schwenkreis. A Logical Clock Based Discovery of Patterns. Int. J. Data Sci. Anal. 2021, 7(4), 98-108. doi: 10.11648/j.ijdsa.20210704.11

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Friedemann Schwenkreis. A Logical Clock Based Discovery of Patterns. Int J Data Sci Anal. 2021;7(4):98-108. doi: 10.11648/j.ijdsa.20210704.11

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@article{10.11648/j.ijdsa.20210704.11,
  author = {Friedemann Schwenkreis},
  title = {A Logical Clock Based Discovery of Patterns},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {4},
  pages = {98-108},
  doi = {10.11648/j.ijdsa.20210704.11},
  url = {https://doi.org/10.11648/j.ijdsa.20210704.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210704.11},
  abstract = {This paper focusses on aspects of applied data mining in the context of team handball. It presents an approach to transform the collected data of team handball matches into formats that allow the use of classification and methods to search for association rules. To be able to search for patterns at arbitrary times of matches a concept of a logical clock is introduced, which becomes an essential part of the data preparation. The applied data mining methods are described in detail using RapidMiner processes and their settings. However, the approach is independent of the used data mining tool. Based on the results of the data mining processes, the applicability of data mining techniques in the given context will be discussed. Particularly it will be shown that rule-based results have significant advantages compared to approaches using support vector machines in the given context. The results are also compared based on the logical clock which will show how patterns evolve over time in case of team handball. We will show that the overall prediction accuracy of a model is not the primary concern in the chosen application area. It is rather to discover rules which clearly help to identify the need for action. The concept of time is crucial in this context because rules are less helpful if they are detected when the game is over, and we are at the end of a slippery slope which could have been prevented long before.},
 year = {2021}
}

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TY - JOUR
T1 - A Logical Clock Based Discovery of Patterns
AU - Friedemann Schwenkreis
Y1 - 2021/08/11
PY - 2021
N1 - https://doi.org/10.11648/j.ijdsa.20210704.11
DO - 10.11648/j.ijdsa.20210704.11
T2 - International Journal of Data Science and Analysis
JF - International Journal of Data Science and Analysis
JO - International Journal of Data Science and Analysis
SP - 98
EP - 108
PB - Science Publishing Group
SN - 2575-1891
UR - https://doi.org/10.11648/j.ijdsa.20210704.11
AB - This paper focusses on aspects of applied data mining in the context of team handball. It presents an approach to transform the collected data of team handball matches into formats that allow the use of classification and methods to search for association rules. To be able to search for patterns at arbitrary times of matches a concept of a logical clock is introduced, which becomes an essential part of the data preparation. The applied data mining methods are described in detail using RapidMiner processes and their settings. However, the approach is independent of the used data mining tool. Based on the results of the data mining processes, the applicability of data mining techniques in the given context will be discussed. Particularly it will be shown that rule-based results have significant advantages compared to approaches using support vector machines in the given context. The results are also compared based on the logical clock which will show how patterns evolve over time in case of team handball. We will show that the overall prediction accuracy of a model is not the primary concern in the chosen application area. It is rather to discover rules which clearly help to identify the need for action. The concept of time is crucial in this context because rules are less helpful if they are detected when the game is over, and we are at the end of a slippery slope which could have been prevented long before.
VL - 7
IS - 4
ER -

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Author Information

Friedemann Schwenkreis

Business Information Systems, Baden-Wuerttemberg Cooperative State University, Stuttgart, Germany

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APA Style

Friedemann Schwenkreis. (2021). A Logical Clock Based Discovery of Patterns. International Journal of Data Science and Analysis, 7(4), 98-108. https://doi.org/10.11648/j.ijdsa.20210704.11

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ACS Style

Friedemann Schwenkreis. A Logical Clock Based Discovery of Patterns. Int. J. Data Sci. Anal. 2021, 7(4), 98-108. doi: 10.11648/j.ijdsa.20210704.11

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AMA Style

Friedemann Schwenkreis. A Logical Clock Based Discovery of Patterns. Int J Data Sci Anal. 2021;7(4):98-108. doi: 10.11648/j.ijdsa.20210704.11

Copy | Download

@article{10.11648/j.ijdsa.20210704.11,
  author = {Friedemann Schwenkreis},
  title = {A Logical Clock Based Discovery of Patterns},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {4},
  pages = {98-108},
  doi = {10.11648/j.ijdsa.20210704.11},
  url = {https://doi.org/10.11648/j.ijdsa.20210704.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210704.11},
  abstract = {This paper focusses on aspects of applied data mining in the context of team handball. It presents an approach to transform the collected data of team handball matches into formats that allow the use of classification and methods to search for association rules. To be able to search for patterns at arbitrary times of matches a concept of a logical clock is introduced, which becomes an essential part of the data preparation. The applied data mining methods are described in detail using RapidMiner processes and their settings. However, the approach is independent of the used data mining tool. Based on the results of the data mining processes, the applicability of data mining techniques in the given context will be discussed. Particularly it will be shown that rule-based results have significant advantages compared to approaches using support vector machines in the given context. The results are also compared based on the logical clock which will show how patterns evolve over time in case of team handball. We will show that the overall prediction accuracy of a model is not the primary concern in the chosen application area. It is rather to discover rules which clearly help to identify the need for action. The concept of time is crucial in this context because rules are less helpful if they are detected when the game is over, and we are at the end of a slippery slope which could have been prevented long before.},
 year = {2021}
}

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