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Spectral clustering is a technique used in machine learning and data analysis to group similar data points into clusters based on the eigenvalues and eigenvectors of a similarity matrix. It leverages the geometric structure of data in a high-dimensional space by transforming it into a lower-dimensional space, where traditional clustering methods like k-means can be more effectively applied. This approach is particularly useful for identifying complex cluster shapes that may not be well represented by traditional methods.
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