Probabilistic graphical models like Markov Logic networks (Richardson & Domingos, 2006;Domingos et al., 2007;Domingos & Lowd, 2009) or factor graphs (Loeliger, 2008;Koller & Friedman, 2009) take into consideration constraints such as transitivity, while spectral clustering methods (Luxburg, 2007) implicitly consider transitivity in the underlying eigenspace decomposition, but suffer from high computational complexity.
all methods need to precompute features for the data points and similarity values between all pairs of data points.