There are several techniques that can be employed to simplify complex datasets in catalysis:
Dimensionality Reduction: Techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are used to reduce the number of variables while preserving important information. Clustering: Methods like K-means clustering and hierarchical clustering group similar data points together, making patterns more discernible. Feature Selection: Identifying and using only the most relevant features of the dataset to simplify analysis without losing significant information. Data Visualization: Tools such as heat maps, scatter plots, and 3D plots help in visualizing complex data in a more understandable form.
