Spectral clustering is a graph-based algorithm for finding k arbitrarily shaped clusters in data. The technique involves representing the data in a low dimension. In the low dimension, clusters in the data are more widely separated, enabling you to use algorithms such as k-means or k-medoids clustering. This low dimension is based on eigenvectors of a Laplacian matrix. A Laplacian matrix is one way of representing a similarity graph that models the local neighborhood relationships between data points as an undirected graph. You can use spectral clustering when you know the number of clusters, but the algorithm also provides a way to estimate the number of clusters in your data.
|Spectral clustering (Since R2019b)
- Partition Data Using Spectral Clustering
Partition data into k clusters by using a graph-based approach.