Description: Exemplar clustering is an unsupervised learning method used to identify patterns and structures in unlabeled datasets. This approach is based on the idea that data can be grouped into clusters, where each cluster contains instances that are similar to each other and different from those in other clusters. Exemplars, which are representative points within the feature space, serve as references to determine the membership of other points to a specific cluster. This method is particularly useful in situations where no prior information about the categories of the data is available, allowing for the discovery of hidden relationships and effective information segmentation. Key characteristics of exemplar clustering include the ability to handle large volumes of data, flexibility to adapt to different data shapes, and the possibility of conducting exploratory analysis. Its relevance lies in its application in various fields such as market segmentation, biology, anomaly detection, and image processing, where pattern identification and data grouping are essential for informed decision-making.
