What are the Challenges in Clustering Cancer Data?
Although clustering is a powerful tool, it comes with several challenges:
High Dimensionality: Cancer data often involves a large number of features, which can complicate the clustering process. Heterogeneity: Cancer is highly heterogeneous, making it difficult to find clear clusters. Data Integration: Combining different types of data (e.g., genomic, proteomic, clinical) for clustering can be challenging but is often necessary for comprehensive analysis. Interpretability: The results of clustering must be interpretable and clinically meaningful, which is not always straightforward.
