Self-reconstructive evidential clustering for high-dimensional data

Abstract

Although many algorithms have been presented to tackle the curse of dimensionality in high-dimensional clustering, most of these algorithms require prior knowledge of the number of clusters. Besides, these existing algorithms create only a hard or fuzzy partition for high-dimensional objects, which are often located in highly overlapping areas. The adoption of hard/fuzzy partition ignores the ambiguity in the assignment of objects and may lead to performance degradation. To address these issues, we propose a novel self-reconstructive evidential clustering (SREC) algorithm. After learning the correlations between objects from a self-reconstruction process, SREC provides a human-readable chart. Through this chart, users can select several objects existing in the dataset as the cluster centers, instead of just detecting the number of clusters. Under the framework of evidence theory, SREC derives a more flexible credal partition that improves the fault tolerance of clustering. Ablation study demonstrates the benefits of the self-reconstruction and evidence theory. Comparison experiments on real-world datasets show that SREC consumes competitive running time and performs better than other state-of-the-art algorithms. We also apply SREC in a real-world application scenario to illustrate the rationality of selecting cluster centers by human intervention.