Clustering Algorithms Comparison

In previous lessons, we explored three fundamentally different unsupervised techniques: the centroid-based K-means algorithm, the density-based DBSCAN algorithm, and the hierarchical agglomerative clustering algorithm. While all aim to group similar data points into clusters, each operates under unique assumptions and performs best with different types of data structures. This lesson shifts the focus from how the algorithms work to comparing their performance, strengths, and weaknesses, helping determine the most appropriate clustering approach for a given dataset.

$K$-means

This is a centroid-based algorithm or a distance-based algorithm, where we calculate the distances to assign a point to a cluster. $K$-means starts by randomly selecting $k$ initial centroids and then assigns each data point to the cluster corresponding to the nearest centroid. The centroids are then updated to the mean of the points in the corresponding cluster, and the process is repeated until ...