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Types of clustering algorithms

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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:

  1. 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.
  1. 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.
  1. 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.

 

  1. 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.

 

  1. 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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