Clustering Algorithms

Agglomerative clustering is a hierarchical clustering method that groups data points based on their proximity.

Centroid representation is a method for summarizing data by its center point in various applications, especially in machine learning.

DBScan is a density-based clustering algorithm that identifies clusters in spatial data.

A distance metric quantifies how far apart two data points are in a given space.

The Elbow Method is a technique for determining the optimal number of clusters in a dataset.

Fuzzy C-Means Clustering is a clustering algorithm that allows data points to belong to multiple clusters with varying degrees of membership.

Hierarchical Agglomerative Clustering (HAC) is a method of cluster analysis that seeks to build a hierarchy of clusters.

Intracluster Distance measures the average distance between points in a cluster, indicating cohesion and density.

K-Means Clustering is a popular algorithm used to group data into distinct clusters based on similarity.

K-Means Plus Plus is an advanced algorithm for initializing the K-Means clustering method, improving the convergence speed and clustering quality.

K-Medoids Clustering is a data clustering technique that identifies representative objects from a dataset, minimizing the distance between points.

Local Outlier Factor (LOF) identifies outliers in data by measuring the local density deviation of each data point.

The Mean Shift Algorithm is a clustering technique used to identify dense regions in data by iteratively shifting data points toward the mean.

Normalized Cut is a graph-based method for image segmentation and clustering in AI.