Abstract: This work introduces a novel approach to ensemble clustering by incorporating transfer learning. We address a clustering problem where, in addition to the data being analyzed, we have access to "similar" labeled data. The datasets may have different feature descriptions. Our method revolves around constructing meta-features that capture the structural characteristics of the data and transferring them from the source domain to the target domain.

Cite: Berikov V.
Ensemble Clustering with Heterogeneous Transfer Learning
11th International Conference on Analysis of Images, Social Networks and Texts 28-30 Sep 2023