To support data mining post-processing, which is one of the important procedures in a data mining process, at least 40 indices are proposed to acquire valuable knowledge. However, since their behaviors have never been elucidated, domain experts are required to spend their time to understanding the meanings of each index in a given data mining result. In this paper, we present an analysis of the behavior of objective rule evaluation indices on classification rule sets by principle component analysis (PCA). Therefore, we carried out a PCA to a dataset consisting of the 39 objective rule evaluation indices. In order to obtain the dataset, we calculated the average values of the bootstrap method on 32 classification rule sets learned by information gain ratio. Then, we identified the seven functional groups of the objective indices based on the PCA. Using this result, we discuss a rule evaluation interface for use by human experts.