Description: Structured data clustering is an unsupervised learning approach that focuses on grouping data organized in formats such as tables or graphs. This method allows for the identification of patterns and relationships within datasets that have a defined structure, facilitating the segmentation of information into homogeneous groups. Through clustering algorithms like K-means, DBSCAN, or hierarchical clustering, specific characteristics of the data can be analyzed, such as similarities and differences, to form clusters that represent meaningful groups. The relevance of this approach lies in its ability to simplify the complexity of data, enabling analysts and data scientists to extract valuable insights without the need for predefined labels. Additionally, structured data clustering is fundamental in various applications, from market research to data analysis in diverse fields. In summary, this approach not only helps organize and visualize data but also enhances informed decision-making based on hidden patterns in structured information.
