Description: Binary clustering is an unsupervised learning method used to group data into two distinct clusters. This approach is based on the idea that data can be classified into two opposing categories, facilitating the identification of patterns and relationships in complex datasets. It is often used in situations where a binary decision is required, such as classifying emails as ‘spam’ or ‘not spam’, or identifying whether an object is present or absent in a dataset. The main characteristics of binary clustering include its simplicity and effectiveness in reducing data dimensionality, allowing for clearer visualization and deeper analysis. This method can be implemented through various techniques, such as the K-means algorithm, where a fixed number of clusters is established, in this case, two. The relevance of binary clustering lies in its ability to simplify decision-making and improve efficiency in data processing, making it a valuable tool in fields such as data analysis, artificial intelligence, and machine learning.
