What Techniques are Used in Data Mining for Catalysis?
Several techniques are employed in data mining for catalysis, including:
Machine Learning: Algorithms like neural networks, decision trees, and support vector machines are used to model catalytic processes and predict outcomes. Cluster Analysis: This technique groups similar data points together to identify patterns and correlations in catalytic activity. Regression Analysis: Used to understand the relationship between variables and predict the effect of changes in reaction conditions. Principal Component Analysis (PCA): Reduces the dimensionality of data, making it easier to visualize and interpret complex datasets. Natural Language Processing (NLP): Extracts information from textual data, such as research papers and patents, to identify trends and insights.
