[1]TANG Yiming,CHEN Renhao,LI Bing.A clustering validity index called MAME for the fuzzy c-means algorithm[J].CAAI Transactions on Intelligent Systems,2023,18(5):945-956.[doi:10.11992/tis.202212028]
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A clustering validity index called MAME for the fuzzy c-means algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 5 Page number: 945-956 Column: 学术论文—机器学习 Public date: 2023-09-05
Title:
A clustering validity index called MAME for the fuzzy c-means algorithm
Author(s):
TANG Yiming;  CHEN Renhao;  LI Bing
School of Computer and Information, Hefei University of Technology, Hefei 230601, China
Keywords:
clustering; fuzzy clustering; fuzzy c-means; clustering validity index; internal criteria; external criteria; compactness; separation
CLC:
TP181；TN99
DOI:
10.11992/tis.202212028
Abstract:
The clustering validity index can be used to evaluate the effectiveness of clustering results and determine the number of clusters. However, existing validity indices for fuzzy c-mean algorithm suffer from the inadequate characterization of intracluster compactness and inaccurate measurement of intercluster separability. To address these issues, we proposed a new fuzzy clustering validity index called maximum-mean (MAME), which considers the maximum and mean values and is designed based on two perspectives, intracluster compactness and intercluster separability. First, considering the comprehensive characteristics of the entire dataset, a new expression of fuzzy compactness measure is put forward by calculating the ratio of cases divided into K clusters and one cluster, respectively. Second, by introducing the maximum and mean distance between cluster centers, a new method is proposed for separability measurement. Finally, the MAME index is put forward on the strength of fuzzy compactness measure expression and the separability measure method. Using five UCI and six artificial datasets, MAME is compared with nine other cluster validity indices, including CH, DB, NPC, PE, FSI, XBI, NPE, WLI, and I. The experimental results demonstrate the accuracy and stability of our proposed index, indicating that MAME has good robustness.
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