The correct answer is: B. Analyzing data from different domains or fields to find common patterns.
Cross-domain analysis is a type of data analysis that involves combining data from different domains or fields to find common patterns. This can be done by using a variety of methods, such as data mining, machine learning, and statistical analysis. Cross-domain analysis can be used to solve a variety of problems, such as identifying new trends, improving decision-making, and developing new products and services.
Data aggregation is the process of combining data from multiple sources into a single dataset. This can be done by using a variety of methods, such as data integration, data warehousing, and data mining. Data aggregation can be used to improve the accuracy and efficiency of data analysis.
Model stacking is a machine learning technique that involves combining the predictions of multiple models to improve the accuracy of the overall prediction. This can be done by using a variety of methods, such as bagging, boosting, and stacking. Model stacking can be used to improve the accuracy of predictions in a variety of applications, such as fraud detection, spam filtering, and medical diagnosis.