DTEC: Decision tree-based evidential clustering for interpretable partition of uncertain data

Recently, the evidential clustering has been developed as a promising clustering framework for uncertain data, which generalizes those hard, fuzzy, possibilistic and rough clustering. However, the resulting cluster assignments are less interpretable in terms of human cognition, which limits its applications in those security, privacy or ethic related fields. In this study, the unsupervised decision tree model is introduced into the evidential clustering framework to improve the interpretability of the evidential partition. A Decision Tree-based Evidential Clustering (DTEC) algorithm is developed to build an unsupervised evidential decision tree, which uses the paths from the root node to leaf nodes to achieve the interpretability of each cluster. The proposed algorithm is composed of three procedures, i.e., cutting-point selection, node evidential splitting, and cluster adjustment, in which the first two procedures are carried out iteratively to build a preliminary unsupervised decision tree and the last procedure is designed to adjust the preliminary decision tree if the number of clusters is available. Both synthetic and real datasets are used to evaluate the performance of the proposed algorithm, and the experimental results demonstrate the good performance of the proposal compared with some representative fuzzy, evidential or decision tree-based clustering algorithms.