Clustering is an unsupervised machine learning technique used to group data points based on their similarity or common features. The primary objective of clustering is to identify the underlying structure in the data, separating it into different groups or clusters. Each cluster consists of data points that are more similar to each other than to data points in other clusters.

There are various clustering algorithms, and they can be broadly categorized into the following types:

1. Hierarchical clustering: This type of clustering builds a tree-like structure (dendrogram) to represent the hierarchy of clusters. Hierarchical clustering can be further divided into two subcategories: agglomerative (bottom-up) and divisive (top-down). In agglomerative clustering, each data point starts as an individual cluster, and clusters are iteratively merged based on their similarity. In divisive clustering, the entire dataset starts as a single cluster, and clusters are iteratively split until each data point forms its own cluster.
2. Partitioning-based clustering: These algorithms aim to partition the data into a predefined number of clusters by optimizing a given criterion. The most popular partitioning-based clustering algorithm is k-means, which assigns each data point to the nearest centroid (the mean of data points in a cluster) and iteratively updates the centroids until convergence. Another example is the k-medoids algorithm, which is similar to k-means but uses medoids (representative data points) instead of centroids.
3. Density-based clustering: These algorithms define clusters based on dense regions of data points, separated by areas of lower point density. One widely-used density-based clustering algorithm is DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which identifies clusters by grouping data points that are closely packed together and treating sparse areas as noise.
4. Grid-based clustering: These algorithms divide the data space into a finite number of grid cells and perform clustering on the grid structure. An example of a grid-based clustering algorithm is STING (Statistical Information Grid), which uses a hierarchical grid structure and statistical measures to identify clusters.
5. Model-based clustering: These algorithms assume that the data is generated from a mixture of underlying probability distributions, and they attempt to estimate the parameters of these distributions. Gaussian Mixture Models (GMM) and Expectation-Maximization (EM) algorithm are common examples of model-based clustering.

Choosing the appropriate clustering algorithm depends on factors such as the dataset size, dimensionality, the desired number of clusters, and the underlying data distribution. Clustering has applications in various domains, including image processing, natural language processing, bioinformatics, marketing, and anomaly detection, among others.