摘要：In many data mining problems related to business it is hard to obtain labeled instances. Whenthe labeled data set is not large enough the classifiers often perform poor results. Neverthelesssemi-supervised learning algorithms, e.g. clustering based classification can learn fromboth labeled and unlabeled instances. We have planned and implemented a semi-supervisedlearning technique by combining the clustering based classification system with active learning.Our active clustering based classification method first clusters both the labeled and unlabeleddata with the guidance of labeled instances, then queries the label of the most informativeinstances in an active learning cycle and after that classifies the data set. At costbenefit analysis comparing the results of our system with the supervised learning and clusteringbased classification it can be concluded that our solution saves the largest cost.
