K-Means Clustering is a widely used clustering algorithm that divides data points into a specified number of clusters based on their similarities.

Clustering aims to uncover inherent patterns and relationships within data by grouping similar data points together.

Euclidean Distance is a commonly used measure of similarity between data points, calculated as the square root of the sum of squared differences between their respective features.

The 'K' parameter in K-Means Clustering specifies the number of clusters into which the data points will be divided.

DBSCAN is a density-based clustering algorithm that identifies clusters as regions of high density separated by regions of low density.

The elbow method is used to determine the optimal number of clusters by identifying the point at which the increase in the number of clusters leads to a significant decrease in the sum of squared errors.

Agglomerative Hierarchical Clustering is a hierarchical clustering algorithm that starts with each data point as a separate cluster and then iteratively merges the most similar clusters until a single cluster is formed.

The silhouette coefficient is a measure used to evaluate the quality of clustering by considering the similarity of each data point to its own cluster and to other clusters.

Gaussian Mixture Models (GMM) is a probabilistic clustering algorithm that assumes that the data points are generated from a mixture of Gaussian distributions.

Spectral clustering is particularly useful for handling data with non-linear relationships, as it utilizes the eigenvectors of the similarity matrix to identify clusters.

Clustering has a wide range of applications in machine learning, including image segmentation, customer segmentation, fraud detection, and recommendation systems.

K-Means Clustering is sensitive to the initialization of cluster centroids, as different initializations can lead to different clustering results.

BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) is a clustering algorithm designed to handle large datasets efficiently by using a hierarchical approach.

Cluster validation is used to evaluate the quality of clustering by assessing how well the clusters represent the underlying structure of the data.

Sum of Squared Errors (SSE), Silhouette Coefficient, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI) are all commonly used cluster validation metrics.