[2F1-GS-1] Fundamental AI, theory: algorithm

[2F1-GS-1-05] Unsupervised Parameter Validation of Rule mining

〇Kyoji Umemura¹, Shiori Hironaka¹, Ayaka Takamoto¹, Chako Takahashi² (1. Toyohashi University of Tech, 2. Yamagata University)

Keywords:data mining, Low frequency, Regularization

Rule-mining algorithms require specific treatment for the rules wherein their items appear only a few times. Each rule-mining algorithm contains a tuning parameter related to the fewness of the related items. A typical method to determine this type of tuning parameter uses validation data. Since validation data are only available with knowledge of the correct rules, it is difficult to determine the parameter. Observing various histograms of the estimated strength, we find that the histograms should be smooth if the parameter is reasonable. This study proposed an unsupervised method to determine the tuning parameter for rule-mining tasks by the histograms of estimated results varying the parameter without knowledge of the correct rules.