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Volume 7 , Issue 4 , August 2021 , Pages: 98 - 108
A Logical Clock Based Discovery of Patterns
Friedemann Schwenkreis, Business Information Systems, Baden-Wuerttemberg Cooperative State University, Stuttgart, Germany
Received: Jul. 29, 2021;       Accepted: Aug. 7, 2021;       Published: Aug. 11, 2021
DOI: 10.11648/j.ijdsa.20210704.11        View        Downloads  
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.
Keywords
Data Science, Applied Data Mining, Classification, Co-Occurrence Grouping, Team Handball
To cite this article
Friedemann Schwenkreis, A Logical Clock Based Discovery of Patterns, International Journal of Data Science and Analysis. Vol. 7, No. 4, 2021, pp. 98-108. doi: 10.11648/j.ijdsa.20210704.11
Copyright
Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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