Types of clustering algorithms

by IndiaSuccessStories April 11, 2024

written by IndiaSuccessStories April 11, 2024 0 comment

Types of clustering algorithms

There are several types of clustering algorithms, each with its own approach to grouping similar data points together. Here are some common types of clustering algorithms:

K-means Clustering

K-means is one of the most widely used clustering algorithms.
It partitions the data into k clusters by iteratively assigning data points to the nearest cluster centroid and updating the centroids to minimize the sum of squared distances from data points to their respective centroids.
K-means works well for spherical clusters and is computationally efficient.

Hierarchical Clustering

Hierarchical clustering builds a tree-like hierarchy of clusters by iteratively merging or splitting clusters based on similarity.
It can be agglomerative (bottom-up), where each data point starts in its own cluster and clusters are successively merged, or divisive (top-down), where all data points start in one cluster and clusters are successively split.
Hierarchical clustering does not require specifying the number of clusters beforehand and can produce a dendrogram to visualize the clustering process.

Density-based Clustering

Density-based clustering algorithms group together data points that are closely packed in high-density regions and separate sparse regions.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular density-based clustering algorithm that identifies core points, border points, and noise points based on local density.
DBSCAN is robust to noise and can discover clusters of arbitrary shape.

Mean Shift Clustering

Mean Shift clustering is a non-parametric clustering algorithm that does not require specifying the number of clusters beforehand
It works by iteratively shifting the centroids of clusters towards the mode of the data distribution, where the density of data points is highest.
Mean Shift clustering can automatically determine the number of clusters and is particularly useful for applications with irregularly shaped clusters.

Agglomerative Clustering

Agglomerative clustering is a type of hierarchical clustering algorithm that starts with each data point as a single cluster and successively merges clusters based on a distance metric.
It can use different linkage criteria, such as Ward's linkage, complete linkage, or average linkage, to determine the distance between clusters during merging.

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Types of clustering algorithms

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