Clustering helps group similar objects together, revealing patterns in data. With growing data complexity, subspace clustering is crucial. This study tackles challenges in finding clusters in high-dimensional data by creating a new method to measure similarity between data points. By focusing on similarities in lower-dimensional spaces and combining them, the method can be used with different clustering algorithms. The researchers developed an algorithm that can identify clusters in overlapping subspaces, improving clustering quality in complex datasets.