Clustering algorithms

Clustering is a type of unsupervised learning algorithm that is used to group similar objects or data points together. In clustering, we don’t have any predefined labels or categories for the data. Instead, the algorithm identifies groups of similar data points based on their characteristics or features.

There are several clustering algorithms, but they can be broadly classified into two categories: hierarchical clustering and partitioning clustering.

  1. Hierarchical clustering: In hierarchical clustering, we start with each data point as a separate cluster, and then merge them together based on their similarity. Hierarchical clustering can be agglomerative (bottom-up) or divisive (top-down).
  2. Partitioning clustering: In partitioning clustering, we start by randomly assigning each data point to a cluster, and then iteratively reassign data points to different clusters to maximize some objective function such as minimizing the sum of squared distances between data points and their assigned cluster centers. The most common partitioning clustering algorithm is k-means.

Other clustering algorithms include density-based clustering, model-based clustering, and spectral clustering.

Clustering algorithms are widely used in a variety of applications, including customer segmentation, image segmentation, anomaly detection, and recommendation systems.

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