Methods in data clustering

Charley Parker & Mark Coffey
Data clustering methods explored included: K-means algorithm, which minimizes the distances from all data points to the centroid of each point's associated cluster; power iteration, which approximates eigenvectors of a similarity matrix used to embed the data into a space where K-means can be useful; and wordplay for clustering a set of documents and using a word count to construct a similarity matrix.
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