Partitioning-based clustering algorithms with MATLAB

Partitioning-based clustering is a type of clustering algorithm that aims to divide a dataset into distinct groups or partitions. In this approach, each data point is assigned to exactly one cluster, and the goal is to minimize the intra-cluster distance while maximizing the inter-cluster distance. The most popular partitioning-based clustering algorithms include k-medoids, fuzzy c-means, and hierarchical k-means. These algorithms vary in their approach and objectives, but they all aim to partition the data into well-separated clusters based on some distance or similarity measure.

Introducing the k-means algorithm

One of the most well-known partitioning-based clustering algorithms is k-means. In k-means clustering, the algorithm attempts to partition the data into k clusters, where k is a predefined number specified by the user. The algorithm iteratively assigns data points to the nearest cluster centroid and recalculates the...