  CFSFDP (clustering by fast search and find of density peaks) is recently
developed density-based clustering algorithm. Compared to DBSCAN, it needs less
parameters and is computationally cheap for its non-iteration. Alex. at al have
demonstrated its power by many applications. However, CFSFDP performs not well
when there are more than one density peak for one cluster, what we name as "no
density peaks". In this paper, inspired by the idea of a hierarchical
clustering algorithm CHAMELEON, we propose an extension of CFSFDP,E_CFSFDP, to
adapt more applications. In particular, we take use of original CFSFDP to
generating initial clusters first, then merge the sub clusters in the second
phase. We have conducted the algorithm to several data sets, of which, there
are "no density peaks". Experiment results show that our approach outperforms
the original one due to it breaks through the strict claim of data sets.
