在网球数据中寻找最大非冗余关联规则
Finding Maximal Non-Redundant Association Rules in Tennis Data
摘要：
关联规则的概念在数据采掘中是众所周知的。但通常不考虑冗余和包容，标准方法产生数千甚至数百万的关联规则。如果没有进一步的信息或挖掘后的方法，那么这些大量的规则对于领域专家来说通常是无用的--这是臭名昭著的模式爆炸问题的一个实例。在这项工作中，我们提出了一个新的定义的冗余和包容的基础上的信心和支持的规则，并提出后挖掘修剪一组关联规则。在一个案例研究中，我们应用我们的方法从时空数据挖掘关联规则。这些数据表示网球比赛中球的轨迹-更准确地说，是网球击中地面的点/时间。我们的目标是分析球员的策略，并试图通过查看产生的关联规则来提高他们的表现。该方法具有一定的通用性，也可应用于具有类似结构的其他时空数据。
英文摘要：
The concept of association rules is well--known in data mining. But often redundancy and subsumption are not considered, and standard approaches produce thousands or even millions of resulting association rules. Without further information or post--mining approaches, this huge number of rules is typically useless for the domain specialist -- which is an instance of the infamous pattern explosion problem. In this work, we present a new definition of redundancy and subsumption based on the confidence and the support of the rules and propose post-- mining to prune a set of association rules. In a case study, we apply our method to association rules mined from spatio--temporal data. The data represent the trajectories of the ball in tennis matches -- more precisely, the points/times the tennis ball hits the ground. The goal is to analyze the strategies of the players and to try to improve their performance by looking at the resulting association rules. The proposed approach is general, and can also be applied to other spatio--temporal data with a similar structure.
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