Sparsity Fuzzy C-Means Clustering with Principal Component Analysis Embedding

The clustering method has been widely used in data mining, pattern recognition, and image identification. Fuzzy c-means (FCM) is a soft clustering method that introduces the concept of membership. In this method, the fuzzy membership matrix is obtained by calculating the distance between data points...

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Vydáno v:IEEE transactions on fuzzy systems Ročník 31; číslo 7; s. 1 - 13
Hlavní autoři: Chen, Jingwei, Zhu, Jianyong, Jiang, Hongyun, Yang, Hui, Nie, Feiping
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Clustering
Clustering algorithms
Clustering methods
Data analysis
Data mining
Data points
Dimensionality reduction
Embedding
Feature extraction
Fuzzy c-means (FCM)
Iterative methods
Noise sensitivity
Optimization
outliers
Outliers (statistics)
Pattern recognition
Principal component analysis
principal component analysis (PCA)
Principal components analysis
Robustness
Sparsity
ISSN:1063-6706, 1941-0034
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Shrnutí:The clustering method has been widely used in data mining, pattern recognition, and image identification. Fuzzy c-means (FCM) is a soft clustering method that introduces the concept of membership. In this method, the fuzzy membership matrix is obtained by calculating the distance between data points in the original space. However, these methods may yield suboptimal results owing to the influence of redundant features. Moreover, FCM is always sensitive to noise points and heavily subject to outliers. In this paper, we propose a method called sparsity FCM clustering with principal component analysis embedding (P_SFCM). We simultaneously conduct principal component analysis (PCA) and membership learning, and then add an additional weighting factor for each data point. The goal of this operation is to identify the noise or outliers. Overall, the benefit of our framework is that it retains most of the information in the subspace while improving the robustness of the noise. In this paper, we employ an iterative optimization algorithm to efficiently solve our model. To verify the reliability of the proposed method, we conduct a convergence analysis, noise robustness analysis, and multi-cluster experiments. Furthermore, comparative experiments are conducted on both synthetic and real benchmark datasets. The experimental results show that the P_SFCM is competitive with comparable methods.
Bibliografie:ObjectType-Article-1
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2022.3217343