Factor evaluation model based on entropy method and spearman correlation analysis and ISODATA clustering algorithm

Making objective and reasonable evaluation for goal objects is the base for making the right decisions during decision-making process in research fields. Newly sprouted things emerge and the measuring method keeps on refining and improving. As a result, the traditional evaluation model does not satisfy the requirements of the trend well on versatility and objectivity and multiple-factor analysis and process. Faced with this situation, it is significant to build an objective evaluation model with multiple-factor analysis processing capacity. In general, the factors are treated as vectors in this paper and all factors will be processed in horizontal and vertical perspective. Spearman correlation analysis and entropy method will be used to calculate the factor value in horizontal perspective. The ISODATA clustering algorithm will be used to combine the properties of factor in vertical perspective. Then the final results are obtained. The model proposed in this paper evaluates several experimental data from different research fields. Meanwhile, the evaluating results are found to be identical to real situation, which proves the versatility and objectivity of this model.