The Mahout implementation of spectral clustering requires an affinity matrix as the input from the user, and it uses the K-means algorithm for the final clustering. Usually, Mahout clustering consists of the following steps:

- User takes a matrix of k*n-dimensional data to which he wants to cluster.
- User will have to create a similarity matrix from the original data matrix. This will be a k*k transformation of the original matrix based on how the points are related to each other.
- From the similarity matrix, an affinity matrix needs to be created. Mahout takes a type of Hadoop-backed affinity matrix as an input in the form of a text file. This is a weighted, undirected graph. Each line of a text file represents ...

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