Constrained Clustering

Constrained Clustering is a method in cluster analysis that considers prior knowledge, grouping data points by introducing constraint conditions to ensure that the clustering results meet specific business or research requirements. This technique aims to enhance the accuracy and interpretability of clustering, and it is widely applied in data mining, machine learning, and pattern recognition, particularly when dealing with complex data structures and high-dimensional data.