A Data Mining Framework for Tea Price Evaluation

Tea price evaluation is a key issue in the process of tea trade. The traditional tea price evaluating methods mainly depend on the experience of tea experts. The evaluating results using these methods are usually unstable and imprecise. So far, how to develop an automatic tea price evaluating system is still a challenging work. In this paper, we propose a data-mining-based tea price evaluation framework, which incorporates anomaly detection, feature importance analysis, and classification forecast models. Experimental evaluation on the real Tie-guan-yin tea database demonstrates the effectiveness of our proposed framework.