Fixed point results on complex-valued metric spaces for fuzzy mappings

Hanafy, I. M.; Kamal, A.; Mohamed, Asmaa

doi:10.21608/ajbas.2021.94627.1066

Cited by 1 publication

(3 citation statements)

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“…Tese operators extend the classical morphological operators by considering the spatial relationships between elements within a neighborhood. We explore how neighborhood morphological operators can capture more detailed information about the local structure, enabling more precise analysis and processing [15].…”

Section: Mathematical Morphologymentioning

confidence: 99%

“…Neighborhood Mathematical Morphology. By using the concept of the topological neighborhood, the defnition of neighborhood-dilation and neighborhood-erosion operations; this idea is assigned to a structure element that lies in the neighborhood of each point instead of the only one structure element for the universe [15].…”

Section: Te Erosionmentioning

confidence: 99%

“…In this section, we introduce a clustering algorithm based on the morphological concepts outlined earlier in [15], including the neighborhood-erosion operator, neighborhooddilation operator, and the morphological accuracy measure. Tis proposed algorithm determines the centroid of each cluster, with the number of clusters decided upon by the user.…”

Section: Morphological Accuracy Cluster (Mac)mentioning

confidence: 99%

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Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis

Azzam,

Maghrabi,

El-Naqeeb

et al. 2024

Applied Computational Intelligence and Soft Computing

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In today’s data-driven world, we are constantly exposed to a vast amount of information. This information is stored in various information systems and is used for analysis and management purposes. One important approach to handle these data is through the process of clustering or categorization. Clustering algorithms are powerful tools used in data analysis and machine learning to group similar data points together based on their inherent characteristics. These algorithms aim to identify patterns and structures within a dataset, allowing for the discovery of hidden relationships and insights. By partitioning data into distinct clusters, clustering algorithms enable efficient data exploration, classification, and anomaly detection. In this study, we propose a novel centroid-based clustering algorithm, namely, the morphological accuracy clustering algorithm (MAC algorithm). The proposed algorithm uses a morphological accuracy measure to define the centroid of the cluster. The empirical results demonstrate that the proposed algorithm achieves a stable clustering outcome in fewer iterations compared to several existing centroid-based clustering algorithms. Additionally, the clusters generated by these existing algorithms are highly susceptible to the initial centroid selection made by the user.

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Section: Mathematical Morphologymentioning

confidence: 99%

Section: Te Erosionmentioning

confidence: 99%

Section: Morphological Accuracy Cluster (Mac)mentioning

confidence: 99%

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