What technique is used to group similar data points together without prior labeling?

The technique used to group similar data points together without prior labeling is clustering. Clustering is an unsupervised learning method in which the algorithm identifies patterns and structures in a dataset by grouping data points that are similar to each other. This is done based on the inherent characteristics of the data, rather than relying on labeled examples.

In clustering, the goal is to maximize the similarity within each cluster while minimizing the similarity between different clusters. This approach is particularly useful in scenarios where the data lacks predefined categories, allowing analysts to discover natural groupings or clusters in the data.

This technique is often employed in various fields such as marketing, biology, and social network analysis, where understanding the relationships and groupings within data can yield valuable insights. By using clustering, organizations can segment customers, identify patterns in behavior or characteristics, and make data-driven decisions based on the naturally occurring groupings in their data.

In contrast, classification requires prior labeling of data points to predict the category of new instances, regression analysis focuses on predicting continuous values, and data integration involves combining data from different sources without grouping based on similarities. Each of these methods serves different purposes in data analysis and cannot be used interchangeably with clustering.