A Logical Clock Based Discovery of Patterns

Friedemann Schwenkreis

doi: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.

Highlights

Innovative team handball coaches are looking for support by modern analysis methods to be able to make information-based decisions during team handball matches
As introduced in previous papers, the focus of our work is to discover patterns in the context of team handball matches using classification and co-occurrence grouping
Several data mining techniques seem to be helpful to solve classification problems and usually the emphasis is on the optimization of the prediction accuracy when selecting a classification technique

Summary

Introduction

Innovative team handball coaches are looking for support by modern analysis methods to be able to make information-based decisions during team handball matches. Patterns extracted from data of past games would be a perfect basis for that. The patterns need to be recognizable and applicable for coaches in future matches and they need to be convertible into actions as early in a match as possible. There is a significant number of publications in the area of applied data mining in the context of sports like soccer, basketball, baseball, and ice hockey. It is impossible to cover all of that work, but Schumaker et al contains a good introduction into the field [1]. Brefeld et al contains latest developments in the area [2]. The major difference of the work described in this paper is the sport itself, which differs significantly from the above-mentioned sports

Objectives

Findings

Discussion

Conclusion