A novel data transformation approach for improved cluster performance

We compare the performance of a new feature scaling approach against conventional feature scaling techniques for clustering. Our approach creates a more well-defined cluster structure using a robust weighted average that incorporates the influence of distance between sample points. We show with empirical evaluation metrics that our approach leads to the formation of better-quality clusters. This is demonstrated across multiple datasets and metrics.