Despite its advantages, k-means clustering has some limitations:
Choice of k: Selecting the appropriate number of clusters (k) can be challenging and may require domain knowledge or additional methods such as the elbow method or silhouette analysis. Sensitivity to initial centroids: The algorithm's performance depends on the initial placement of centroids, which can lead to different clustering results. Multiple runs with different initializations may be needed. Assumption of spherical clusters: k-means assumes that clusters are spherical and equally sized, which may not always be true for catalytic data. Other clustering methods, like hierarchical clustering or DBSCAN, might be more suitable in such cases. Scalability issues: While k-means can handle large datasets, its performance can degrade with extremely large datasets or high-dimensional data, requiring more computational resources.
