Clustering aims to group similar data objects together while keeping dissimilar objects apart. Various bioinspired algorithms have been developed to address different challenges in clustering tasks. One promising approach is the use of self-organizing neural networks, which can adapt and learn the underlying patterns in the data. Transfer Learning (TL) has also gained attention for its ability to leverage knowledge from one domain to improve learning in another. In this context, a Transfer Learning Unsupervised Network (TRUNC) is proposed, integrating a self-organizing network with TL to enhance clustering performance. This paper introduces TRUNC, presents a sensitivity analysis of the algorithm to the transfer learning term, and an evaluation of its effectiveness when applied to synthetic data.