  In this note, we provide a refined analysis of Mitra's algorithm
\cite{mitra2008clustering} for classifying general discrete mixture
distribution models. Built upon spectral clustering
\cite{mcsherry2001spectral}, this algorithm offers compelling conditions for
probability distributions. We enhance this analysis by tailoring the model to
bipartite stochastic block models, resulting in more refined conditions.
Compared to those derived in \cite{mitra2008clustering}, our improved
separation conditions are obtained.
