Which Techniques are Used in Data Mining for Catalysis?
Several data mining techniques are commonly used in the context of catalysis:
Machine Learning: Algorithms such as neural networks, decision trees, and support vector machines are used to predict catalytic behavior and optimize catalyst design. Clustering: This technique groups similar data points together, helping to identify patterns and correlations within catalytic data. Principal Component Analysis (PCA): PCA reduces the dimensionality of datasets while preserving important information, making it easier to visualize and interpret complex data. Regression Analysis: Regression models are used to establish relationships between variables and predict outcomes based on input data. Association Rule Mining: This technique discovers interesting relationships between variables in large datasets, aiding in the identification of key factors influencing catalytic performance.
