  Although the validation step can appear crucial in the case of clustering
adopting fuzzy approaches, the problem of the partition validity obtained by
those adopting the hard ones was not tackled. To cure this problem, we propose
in this paper fuzzy-hard mapping processes of clustering while benefitting from
those adopting the fuzzy case. These mapping processes concern: (1) local and
global clustering evaluation measures: the first for the detection of the
"worst" clusters to merging or splitting them. The second relates to the
evaluation of the obtained partition for each iteration, (2) merging and
splitting processes taking into account the proposed measures, and (3)
automatic clustering algorithms implementing these new concepts.
