The correct answer is: Both A and C.
Clustering is a data mining task that groups unlabeled data points together based on their similarity. Classification is a data mining task that assigns labels to unlabeled data points. Association rule mining is a data mining task that finds associations between variables in a data set.
Clustering is a descriptive model because it does not make any predictions about future data. It simply groups data points together based on their similarity. Classification is also a descriptive model because it does not make any predictions about future data. It simply assigns labels to data points based on their similarity to other data points. Association rule mining is a predictive model because it finds associations between variables in a data set. These associations can be used to make predictions about future data.
Here are some examples of descriptive models:
- Clustering: A clustering algorithm might be used to group customers into different segments based on their purchase history.
- Classification: A classification algorithm might be used to predict whether a customer is likely to churn (cancel their subscription).
- Association rule mining: An association rule mining algorithm might be used to find associations between products that are often purchased together.
Here are some examples of predictive models:
- Regression: A regression model might be used to predict the price of a house based on its features.
- Time series forecasting: A time series forecasting model might be used to predict the number of customers that will visit a store in the next week.
- Classification: A classification model might be used to predict whether a customer is likely to respond to a marketing campaign.