DisRFC: a dissimilarity-based Random Forest Clustering approach

作者：

Highlights：

• We present the first dissimilarity-based random forest-clustering approach.

• The approach works only with distances, thus appropriate for non-vectorial objects.

• The approach works also with non-metric dissimilarities.

• We present a novel unsupervised RF variant working only with dissimilarities.

摘要

•We present the first dissimilarity-based random forest-clustering approach.•The approach works only with distances, thus appropriate for non-vectorial objects.•The approach works also with non-metric dissimilarities.•We present a novel unsupervised RF variant working only with dissimilarities.

论文关键词：Random forests clustering,Dissimilarities,Unsupervised learning,Clustering,Non-vectorial representation

论文评审过程：Received 21 December 2021, Revised 18 August 2022, Accepted 6 September 2022, Available online 11 September 2022, Version of Record 23 September 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.109036