Optimal Pattern Discovery to Reveal the High Yield Inhibition Factor of Soybeans

Abstract

Our research group is working on soybeans which the quantity of yielding is difficult to predict. We focus on the common characteristics observed at multiple cultivation points, in order to examine methods to acquire new knowledge in deciding the work based on the amount of yields. Our previous study has examined a method to discover optimal patterns using qualitative value of cultivation data. In this method, one qualitative value is assigned based on the boundary value. However, by typically generating qualitative values around the boundary value, different qualitative values are given, even if they are almost the same value, which tremendously affect the calculation of the optimal patterns. In this study, by considering ambiguity in boundary values when generating qualitative values, we discover new knowledge that was not found by the previous method. This method was applied to actual data. As a result, it was verified that the elements of the frequently appearing pattern have changed, and the pattern with the lower evaluation value has appeared in the higher rank. Moreover, by analyzing the optimal pattern extracted by the proposed method and the existing method, the trend of the well-known factors for high yield was discovered and factors that prevent high yield from the optimal pattern were eliminated.