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Model Evaluation

Clustering

Clustering is a technique of partitioning a set of points in a space into subsets, where each cluster consists of “nearby” points. In general, a solution to any clustering problem comes up with cluster centers that define the clusters. A cluster is the set of data points that are close to a particular center (Cluster center). Using this observation, it is relatively easy to cluster points in two or three dimensions. However, clustering is not so easy in higher dimensions.

Here are the top 5 clustering algorithms :

  1. K-Means Clustering
  2. Mean-Shift Clustering
  3. Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
  4. Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
  5. Agglomerative Hierarchical Clustering
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