What are the Challenges in Using K Means Clustering in Epidemiology?
Despite its utility, K Means Clustering has several challenges in the context of epidemiology:
Selection of K: Determining the optimal number of clusters (K) is often challenging and may require additional methods like the Elbow Method or Silhouette Analysis. Data Quality: The quality of clustering results heavily depends on the quality and completeness of the data, which can often be an issue in epidemiological studies. Interpretation of Clusters: The clusters formed need to be interpreted in a meaningful way, which sometimes can be subjective and require domain expertise. Computational Complexity: For large datasets, the algorithm can become computationally expensive, necessitating efficient implementations and possibly parallel processing.
