Description: Aggregate Clustering is an approach within unsupervised learning that seeks to improve the accuracy and robustness of clustering results by combining multiple outcomes from different clustering algorithms. This method is based on the premise that each algorithm can capture different aspects of the data structure, and by merging their results, a more complete and accurate representation of the relationships among the data can be obtained. The main characteristics of Aggregate Clustering include the diversity of the algorithms used, the way results are combined (either through voting, averaging, or hierarchical methods), and the ability to handle noisy data or outliers. This approach is particularly relevant in contexts where data is complex and multidimensional, as it helps mitigate the limitations inherent to a single clustering algorithm. In summary, Aggregate Clustering presents itself as a powerful tool for enhancing the quality of data analysis, facilitating the identification of patterns and hidden structures in large and varied datasets.
