Distance Metrics
Distance metrics are mathematical functions that measure the similarity or dissimilarity between two or more data points. In data science and artificial intelligence, distance metrics are commonly used to cluster similar data points together, classify data points, and reduce the dimensionality of high-dimensional data. Distance metrics can be categorized into two main types: Euclidean distance and non-Euclidean distance. Euclidean distance is the straight-line distance between two points in a Euclidean space, while non-Euclidean distance includes other types of distance metrics such as Manhattan distance, Minkowski distance, and cosine similarity. Distance metrics are essential in many machine learning algorithms such as k-nearest neighbors, hierarchical clustering, and principal component analysis.
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