• Makes use of the spectrum (eigenvalues) of the similarity matrix of the data to perform dimensionality reduction before clustering in fewer dimensions. The similarity matrix is provided as an input and consists of a quantitative assessment of the relative similarity of each pair of points in the dataset. -Wikipedia

Is a:

• method

Applications:

• Graph Clustering

Advantages:

• does not make strong assumptions on the forms of the clusters -ASU CSE 575 course
• easy and efficient even for large data sets as long as the similarity graph is sparse
• good clustering results

Disadvantages:

• sensitive to initial parameters