Evaluation of learning costs of rule evaluation models based on objective indices to predict human hypothesis construction phases

In this paper, we present an evaluation of learning costs of rule evaluation models based on objective indices for an iterative rule evaluation support method in data mining post-processing. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to find out valuable knowledge from several thousands of rules obtained with a large dataset with noises. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To estimate learning costs for predicting human interests with objective rule evaluation indices, we have done the two case studies with actual data mining results, which include different phases of human interests. With regarding to these results, we discuss about the relationship between performances of learning algorithms and human hypothesis construction process.