Improved Density Peak Clustering Based on Information Entropy for Ancient Character Images

Abstract

A large number of IoT applications require the use of supervised machine learning, a type of machine learning algorithm that requires data to be labeled before the model can be trained. Because manually labeling large datasets is a time-consuming, error-prone, and expensive task, automated machine learning methods can be used. To tackle the challenge in which an ancient character image needs to be manually labeled, this paper explores the classification method of ancient Chinese character images based on density peak clustering. We design a metric function of density peak clustering and propose an improved density peak clustering method based on information entropy for ancient book image classification. The method enumerates the distance threshold of clustering, then calculates the information entropy of the clustering result, and determines the class distance threshold by analyzing the attenuation of the information entropy, thereby completing the image clustering process. The improved metric function is used to calculate the similarity between images. A greedy strategy is used as the basis of the merging operation of the class members to achieve the purpose of increasing the degree of information entropy attenuation. The experimental results on the dataset of the Yi character images prove that the method can accurately classify unknown character images of ancient books. INDEX TERMS Density peak clustering, information entropy, ancient character image.

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“…The use of only the DBSCAN algorithm cannot directly uncover the strong or weak relationship between the fault information and the data. Considering the heterogeneity of the high-dimensional time-series complex data, the information entropy is combined with the DBSCAN algorithm by calculating the true entropy value of the entity with fault information and the entropy value of the entity data associated with it and then utilizing the absolute difference between the two entropy values to assess the degree of proximity between them [44]. Finally, a threshold is set to assess the strength of the relationship between them.…”