Optimal Pattern Mining from Time Series Cultivation Data of Soybeans for Knowledge Discovery
Recently, we can analyze and store big data by high performance computers. In this paper, we present a method of optimal pattern mining from soybean cultivation data for knowledge discovery by introducing an evaluation function based on differences in the frequency of high yields and low yields. We can discover factors affecting the growth of soybeans by analyzing optimal patterns extracted using evaluation functions. In our proposed method, optimal patterns are enumerated by eliminating elements that decrease the value of evaluation functions from frequent closed patterns. As a result, our experiment showed the efficiency of the proposed method. In addition, we can observe both general and new knowledge by analyzing extracted optimal pattern groups.
TSAA '16