Open Access
Peer-Reviewed
Original Research
Assessing the Quality of Fuzzy Partitions in
Abstract
Many validity indexes have been proposed for evaluating clustering results. They usually have a tendency to fail in selecting the right number of clusters when dealing with overlapping clusters such as the IRIS data. To overcome this limitation, we propose in this paper, a new cluster validity index based on Maximum Entropy Principle, named V MEP. VMEP allows finding the correct number of clusters, and can deal successfully with or without the presence of overlap, even when this later is higher between clusters. Many simulated and real examples are presented, showing the superiority of V MEP to the existing indexes.
Keywords
Computer Science
Information Systems
Software Engineering
Artificial Intelligence
IADIS
Data Analytics
Declarations & Ethics
Funding:
This research received academic dissemination support through ESCAP / JournalsHub publishing programs.
Conflicts of Interest:
The authors declare no competing financial or institutional interests.
Peer Review:
Double-blind peer reviewed by international subject specialists.
License:
Creative Commons Attribution 4.0 International (CC BY 4.0).
How to Cite This Article
APA / MLA / BibTeX
Ammor, et al. (2009). Assessing the Quality of Fuzzy Partitions in. IADIS International Journal on Computer Science and Information Systems, 4(1). https://doi.org/10.33965/ijcsis_2009_v4i1_08
Ammor, et al. "Assessing the Quality of Fuzzy Partitions in." IADIS International Journal on Computer Science and Information Systems, vol. 4, no. 1, 2009. https://doi.org/10.33965/ijcsis_2009_v4i1_08
Ammor, et al. "Assessing the Quality of Fuzzy Partitions in." IADIS International Journal on Computer Science and Information Systems 4, no. 1 (2009). https://doi.org/10.33965/ijcsis_2009_v4i1_08