Novel fuzzy similarity measures and their applications in pattern recognition and clustering analysis

Singh, Surender; Singh, Koushal

doi:10.1007/s41066-023-00393-y

Cited by 8 publications

(1 citation statement)

References 57 publications

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“…Various distance and similarity measures for fuzzy sets, as well as their applications, have been proposed in the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. A theoretical usefulness of similarity measures is that it enables us to classify fuzzy implications into equivalence classes, based on their degree of similarity [15].…”

Section: Introductionmentioning

confidence: 99%

A Distance Measure Between Fuzzy Implications

Hatzimichailidis

2024

Decis. Mak. Adv.

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Section: Introductionmentioning

confidence: 99%

A Distance Measure Between Fuzzy Implications

Hatzimichailidis

2024

Decis. Mak. Adv.

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A robust multi-view knowledge transfer-based rough fuzzy C-means clustering algorithm

Zhao,

Yang,

Liu

et al. 2024

Complex Intell. Syst.

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Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images.

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