Traditional data classification is based only on physical features of input data. They are called low level classification. Data classification by considering not only physical attributes but also pattern formation is denominated high level classification. In this paper, we propose a new technique that performs high level classification by extracting information of networks constructed from the input data. Specifically, we calculate the network entropies before and after the insertion of a data item to be classified. Then, we classify it as belonging to the class which results in the largest increase of the entropy measures. We show that the proposed method can execute classification tasks according to both similarity and pattern formation of input data to reach good results in the experiments with artificial and real data sets. In summary, our technique can calculate how significant a data item is for each class performing a new way to classify data.