What are some examples of clustering machine learning algorithms in Computer Science?

1 K-means clustering

K-means clustering is one of the simplest and most popular clustering algorithms. It aims to partition the data into k clusters, where each data point belongs to the cluster with the nearest mean or centroid. The algorithm works by randomly initializing k centroids, assigning each data point to the closest centroid, and updating the centroids based on the average of the assigned data points. This process is repeated until the centroids converge or a maximum number of iterations is reached. K-means clustering is fast and easy to implement, but it has some drawbacks, such as being sensitive to the initial centroids, the choice of k, and outliers.

2 Hierarchical clustering

Hierarchical clustering is a family of clustering algorithms that build a hierarchy of clusters based on the similarity or dissimilarity between data points. There are two main types of hierarchical clustering: agglomerative and divisive. Agglomerative clustering starts with each data point as a single cluster and merges the closest clusters until a desired number or level of clusters is reached. Divisive clustering starts with all data points in one cluster and splits the clusters recursively until a desired number or level of clusters is reached. Hierarchical clustering can produce a dendrogram, which is a tree-like structure that shows the nested clusters and their distances. Hierarchical clustering is more flexible and informative than k-means clustering, but it is also more computationally expensive and sensitive to noise and outliers.

3 DBSCAN clustering

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that groups data points based on their density and identifies outliers as noise. The algorithm works by defining two parameters: epsilon and minPts. Epsilon is the radius of a neighborhood around a data point, and minPts is the minimum number of data points required to form a dense region. The algorithm then classifies each data point as a core point, a border point, or a noise point. A core point is a data point that has at least minPts data points within its epsilon neighborhood. A border point is a data point that is not a core point but is in the neighborhood of a core point. A noise point is a data point that is neither a core point nor a border point. The algorithm then forms clusters by connecting core points that are in the same neighborhood or reachable by a chain of core points. DBSCAN clustering is robust to outliers and can handle clusters of different shapes and sizes, but it requires careful selection of epsilon and minPts and may not perform well on data with varying densities.

4 Spectral clustering

Spectral clustering is a clustering algorithm that uses the eigenvalues and eigenvectors of a similarity matrix to partition the data into clusters. The algorithm works by constructing a similarity matrix that measures the pairwise affinity between data points, such as the Gaussian kernel or the cosine similarity. The algorithm then applies a dimensionality reduction technique, such as the Laplacian eigenmap or the singular value decomposition, to transform the similarity matrix into a lower-dimensional space that preserves the cluster structure. The algorithm then applies a simple clustering algorithm, such as k-means clustering, to the lower-dimensional space to obtain the final clusters. Spectral clustering can capture complex and non-linear cluster shapes, but it is also computationally intensive and sensitive to the choice of similarity measure and the number of clusters.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?