Forgy's' Algorithm for Clustering?

K-means clustering (k-means for short), also known as Forgy's algorithm, is one of the most well-known methods for data clustering. The goal of k-means is to find k points of a dataset that can best represent the dataset in a certain mathematical sense (to be detailed later).

What is initialization in clustering?

The k-means Cluster Initialization Problem

Centroid initialization, such that the initial cluster centers are placed as close as possible to the optimal cluster centers. Selection of the optimal value for k (the number of clusters, and centroids) for a particular dataset.

Which algorithm is used for clustering?

k-means is the most widely-used centroid-based clustering algorithm. Centroid-based algorithms are efficient but sensitive to initial conditions and outliers. This course focuses on k-means because it is an efficient, effective, and simple clustering algorithm.