# Clustering

## What is Clustering?

Clustering is a machine learning technique that involves grouping similar data points together based on their characteristics or features. Clustering can be used for a variety of applications such as customer segmentation, anomaly detection, and image compression. Several popular clustering algorithms include K-means, Hierarchical, DBSCAN, OPTICS, and Spectral clustering.

## What do K-means, Hierarchical, DBSCAN, OPTICS, and Spectral clustering do?

K-means, Hierarchical, DBSCAN, OPTICS, and Spectral clustering are algorithms for clustering data:

• K-means clustering is an algorithm that partitions the data into a pre-defined number of clusters, minimizing the sum of squared distances between each data point and the centroid of its cluster.

• Hierarchical clustering is an algorithm that creates a hierarchy of clusters by recursively merging or splitting clusters based on a distance metric between the data points.

• DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm that clusters data based on density, identifying areas of high density as clusters and areas of low density as noise.

• OPTICS (Ordering Points To Identify the Clustering Structure) is an algorithm that extends DBSCAN by producing a hierarchical clustering based on density connectivity.

• Spectral clustering is an algorithm that uses the eigenvectors of the Laplacian matrix to transform the data into a lower-dimensional space, where it can be clustered using K-means or another clustering algorithm.

## Some benefits of Clustering

Clustering offers several benefits for grouping similar data points together:

• Data segmentation: Clustering can be used to segment data into groups based on their characteristics or features, enabling more targeted analysis and decision making.

• Anomaly detection: Clustering can be used to detect anomalies in the data, such as data points that do not fit into any cluster or clusters with significantly different characteristics from the others.

• Image compression: Clustering can be used to compress images by grouping similar pixels together and reducing the number of distinct colors.