Partitioning methods are a class of clustering algorithms that divide a dataset into distinct groups or clusters based on specific criteria, optimizing a particular objective function. These methods are essential for identifying inherent structures in data, making them valuable in various fields such as data mining, machine learning, and pattern recognition.