Robinson based his frequency seriation method on a similarity matrix.

A similarity matrix is a matrix of scores which express the similarity between two data points.

Therefore, the similarity matrix for amino acids contains 400 entries (although it is usually symmetric).

One approach has been to empirically generate the similarity matrices.

Scores for aligned characters are specified by a similarity matrix.

When comparing proteins, one uses a similarity matrix which assigns a score to each possible residue.

Using a similarity matrix, the algorithm delivered matches between meanings including a confidence factor.

The next step is to use the two features to construct a binary similarity matrix.

The eigenvector sequences are expressed as the corresponding similarity matrices.

Sometimes it is more convenient to express data as a similarity matrix.