Abstract
The goal of graph-based clustering is to divide a dataset
into disjoint subsets with members similar to each other
from an affinity (similarity) matrix between data. The most
popular method of solving graph-based clustering is spectral clustering. However, spectral clustering has drawbacks. Spectral clustering can only be applied to macroaverage-based cost functions, which tend to generate undesirable small clusters. This study first introduces a novel
cost function based on micro-average. We propose a local
optimization method, which is widely applicable to graphbased clustering cost functions. We also propose an initialguess-free algorithm to avoid its initialization dependency.
Moreover, we present two global optimization techniques.
The experimental results exhibit significant clustering performances from our proposed methods, including 100%
clustering accuracy in the COIL-20 dataset