Unconstrained Fuzzy C-Means Algorithm

Fuzzy C-Means algorithm (FCM) is one of the most commonly used fuzzy clustering algorithm, which uses the alternating optimization algorithm to update the membership matrix and the cluster center matrix. FCM achieves effective results in clustering tasks. However, due to many constraints, the object...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 47; číslo 5; s. 3440 - 3451
Hlavní autoři: Nie, Feiping, Zhang, Runxin, Yu, Weizhong, Li, Xuelong
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2025
Témata:
Clustering algorithms
Convergence
Fuzzy C-Means
gradient descent
Linear programming
local minimum
membership matrix
Noise
Optimization
Sensitivity
Symbols
Time complexity
Vectors
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Shrnutí:Fuzzy C-Means algorithm (FCM) is one of the most commonly used fuzzy clustering algorithm, which uses the alternating optimization algorithm to update the membership matrix and the cluster center matrix. FCM achieves effective results in clustering tasks. However, due to many constraints, the objective function is inconvenient to optimize directly and is prone to converges to a suboptimal local minimum, which affects the clustering performance. In this paper, we propose a minimization problem equivalent to FCM. Firstly, we use the optimal solution when fixing the cluster center matrix to replace the membership matrix, transforming the original constrained optimization problem into an unconstrained optimization problem, thus reducing the number of variables. We then use gradient descent instead of alternating optimization to solve the model, so we call this model UC-FCM. Extensive experimental results show that UC-FCM can obtain better local minimum and achieve superior clustering performance compared to FCM under the same initialization. Moreover, UC-FCM is also competitive compared with other advanced clustering algorithms.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2025.3532357