Description
In recent years, power Iteration clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm.
Power iteration clustering is a scalable and efficient algorithm for clustering points given pointwise mutual affinity values. Internally the algorithm:
computes the Gaussian distance between all pairs of points and represents these distances in an Affinity Matrix
calculates a Normalized Affinity Matrix
calculates the principal eigenvalue and eigenvector
Clusters each of the input points according to their principal eigenvector component value
Details of this algorithm are found within [Power Iteration Clustering, Lin and Cohen]
{www.icml2010.org/papers/387.pdf}Attachments
Issue Links
- blocks
-
SPARK-5503
Example code for Power Iteration Clustering
-
- Resolved
-
- is related to
-
SPARK-11465 Support multiple eigenvectors in power iteration clustering
-
- Resolved
-
-
SPARK-6441 Add Deflation/Schur Complement to Power Iteration Clustering for improved resilience to inter-class collisions
-
- Resolved
-
- links to
