To address the limitations of k-means clustering in catalysis, researchers can consider the following strategies:
Using advanced initialization methods: Techniques like k-means++ can improve the selection of initial centroids, leading to better clustering results. Evaluating multiple k values: Methods such as the elbow method or silhouette analysis can help determine the optimal number of clusters. Combining with other clustering techniques: Hybrid approaches that combine k-means with other clustering methods, like hierarchical clustering or DBSCAN, can provide more robust and meaningful results. Dimensionality reduction: Techniques like PCA (Principal Component Analysis) or t-SNE (t-Distributed Stochastic Neighbor Embedding) can reduce the dimensionality of data, making it more manageable for k-means clustering.
