Other Graph Partition Methods
Spin models have been used for clustering of multivariate data wherein similarities are translated into coupling strengths. The properties of ground state spin configuration can be directly interpreted as communities. Thus, a graph is partitioned to minimize the Hamiltonian of the partitioned graph. The Hamiltonian (H) is derived by assigning the following partition rewards and penalties.
- Reward internal edges between nodes of same group (same spin)
- Penalize missing edges in same group
- Penalize existing edges between different groups
- Reward non-links between different groups.
Additionally, Kernel PCA based Spectral clustering takes a form of least squares Support Vector Machine framework, and hence it becomes possible to project the data entries to a kernel induced feature space that has maximal variance, thus implying a high separation between the projected communities
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