  Approximate spectral clustering (ASC) was developed to overcome heavy
computational demands of spectral clustering (SC). It maintains SC ability in
predicting non-convex clusters. Since it involves a preprocessing step, ASC
defines new similarity measures to assign weights on graph edges. Connectivity
matrix (CONN) is an efficient similarity measure to construct graphs for ASC.
It defines the weight between two vertices as the number of points assigned to
them during vector quantization training. However, this relationship is
undirected, where it is not clear which of the vertices is contributing more to
that edge. Also, CONN could be tricked by noisy density between clusters. We
defined a directed version of CONN, named DCONN, to get insights on vertices
contributions to edges. Also, we provided filtering schemes to ensure CONN
edges are highlighting potential clusters. Experiments reveal that the proposed
filtering was highly efficient when noise cannot be tolerated by CONN.
