Clustering Algorithms
Clustering algorithms are a type of unsupervised machine learning technique used to group similar data points together based on their characteristics. The goal of clustering is to identify patterns and relationships in the data that may not be immediately apparent. Clustering algorithms can be used for a variety of applications, such as customer segmentation, anomaly detection, and image recognition. There are several types of clustering algorithms, including hierarchical clustering, k-means clustering, and density-based clustering. Hierarchical clustering involves creating a tree-like structure of clusters, while k-means clustering involves partitioning the data into a predetermined number of clusters. Density-based clustering identifies clusters based on areas of higher density in the data. Clustering algorithms can be evaluated using metrics such as silhouette score and elbow method.
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