Year: 2012
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Author: Sassi, Minyar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2282064529