An image fusion algorithm based on fuzzy C-means clustering

Gong, Jiaming; Wu, Chengchao; Guo, Liufei; Liu, Wei; Pei, Mengjie

doi:10.1109/icmtma54903.2022.00066

Cited by 1 publication

(3 citation statements)

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“…The DPC algorithm is used as the start point, where the local density ρ of the data point and its minimum distance δ between itself and other points with higher local density still use the previous calculation formulas, namely (7) and (9).…”

Section: Cluster Initialization Methods Induced By Cut-off Distancementioning

confidence: 99%

“…Considering that the average number of neighbors is about 1-2% of the total data, and selecting the smaller one of the two factors, ( 12) is obtained. Then, naturally, we use the new local density calculation formula: The DPC algorithm is used as the start point, where the local density ρ of the data point and its minimum distance δ between itself and other points with higher local density still use the previous calculation formulas, namely (7) and (9).…”

Section: Cluster Initialization Methods Induced By Cut-off Distancementioning

confidence: 99%

“…Among numerous fuzzy clustering algorithms, the fuzzy c-means (FCM) algorithm is one of the most commonly used methods [9][10][11][12][13][14]. In general, FCM attached equal importance to all features of data, establishing a symmetrical structure, which might often be inconsistent with that of the original data.…”

Section: Introductionmentioning

confidence: 99%

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VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm

2023

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Nowadays, most fuzzy clustering algorithms are sensitive to the initialization results of clustering algorithms and have a weak ability to handle high-dimensional data. To solve these problems, we developed the viewpoint-driven subspace fuzzy c-means (VSFCM) algorithm. Firstly, we propose a new cut-off distance. Based on this, we establish the cut-off distance-induced clustering initialization (CDCI) method and use it as a new strategy for cluster center initialization and viewpoint selection. Secondly, by taking the viewpoint obtained by CDCI as the entry point of knowledge, a new fuzzy clustering strategy driven by knowledge and data is formed. Based upon these, we put forward the VSFCM algorithm combined with viewpoints, separation terms, and subspace fuzzy feature weights. Moreover, compared with the symmetric weights obtained by other subspace clustering algorithms, the weights of the VSFCM algorithm exhibit significant asymmetry. That is, they assign greater weights to features that contribute more, which is validated on the artificial dataset DATA2 in the experimental section. The experimental results compared with multiple advanced clustering algorithms on the three types of datasets validate that the proposed VSFCM algorithm has the best performance in five indicators. It is demonstrated that the initialization method CDCI is more effective, the feature weight allocation of VSFCM is more consistent with the asymmetry of experimental data, and it can achieve better convergence speed while displaying better clustering efficiency.

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Section: Cluster Initialization Methods Induced By Cut-off Distancementioning

confidence: 99%

Section: Cluster Initialization Methods Induced By Cut-off Distancementioning

confidence: 99%