What activity groups data members that have similarities?

Clustering is the correct answer because it refers to the process of grouping a set of objects in such a way that objects in the same group, or cluster, are more similar to each other than to those in other groups. This technique is commonly used in data analysis and machine learning to identify patterns or inherent structures in data. Clustering algorithms analyze the characteristics of data points and organize them into clusters based on their similarities, which can help in understanding the underlying structure of the data and is widely applicable in various fields such as market research, image analysis, and social network analysis.

While classification involves assigning predefined labels to data points based on features, it does not inherently group members based on their similarities; rather, it categorizes them into existing classes. Integration refers to combining data from different sources into a single coherent dataset. Estimation relates to predicting a value based on available data but does not focus on grouping similarities among data members. Thus, clustering stands out as the most appropriate activity for grouping similar data members.