What Are the Challenges of Using K-means Clustering in Cancer Research?
While K-means clustering is a powerful tool, it has its limitations. One major challenge is that the algorithm requires the number of clusters (K) to be specified beforehand, which may not always be straightforward. Additionally, K-means clustering is sensitive to the initial placement of centroids, which can lead to different results for different initializations. This variability necessitates multiple runs of the algorithm with different initial conditions to ensure robust results.
