Hybridization of Sine-Cosine Algorithm with K-Means for Pathology Image Clustering

Dhal, Krishna Gopal; Rai, Rebika; Das, Arunita; Ghosh, T. K.

doi:10.1007/978-3-031-22485-0_8

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Accuracy is widely used to evaluate the performance of clustering algorithms. The accuracy calculation is as (6),…”

Section: Clustering Indicatorsmentioning

confidence: 99%

“…After each update of the K-means mean, the cluster can only have a small movement because the mean cannot jump out of the range of the initial cluster [5]. As a result, it is difficult for K-means to obtain the globally optimal cluster and may fall into local optima [6]. To solve this problem, researchers have proposed spectral clustering algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

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Semi-supervised spectral clustering using shared nearest neighbour for data with different shape and density

YouSheng,

Aminuddin,

Hamzah

et al. 2024

IJ-AI

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<p>In the absence of supervisory information in spectral clustering algorithms, it is difficult to construct suitable similarity graphs for data with complex shapes and varying densities. To address this issue, this paper proposes a Semi-supervised Spectral Clustering algorithm based on shared nearest neighbor. The proposed algorithm combines the idea of semi-supervised clustering, adding Shared Nearest Neighbor information to the calculation of the distance matrix, and using pairwise constraint information to find the relationship between two data points, while providing a portion of supervised information. Comparative experiments were conducted on artificial data sets and University of California Irvine machine learning repository datasets. The experimental results show that the proposed algorithm achieves better clustering results compared to traditional K-means and spectral clustering algorithms.</p>

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“…Accuracy is widely used to evaluate the performance of clustering algorithms. The accuracy calculation is as (6),…”

Section: Clustering Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semi-supervised spectral clustering using shared nearest neighbour for data with different shape and density

YouSheng,

Aminuddin,

Hamzah

et al. 2024

IJ-AI

View full text Add to dashboard Cite

show abstract

“…5. In a similar line, EO technology could be additionally stretched to work on diverse optimization problems namely, Energy storage devices, Smart grids, Knowledge discovery systems, Fog systems, Abrupt motion tracking, DNA fragment assembly problems, Electric Car component design, Production planning, Rule mining, Image encryption and decryption, Vehicle routing problems, Shrimp freshness detection, Signal denoising, Pathology Image Clustering [244,245], Work scheduling, Multi-objective feature selection, Smart home applications etc. 6.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Rai

Dhal

2023

Arch Computat Methods Eng

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There have been many algorithms created and introduced in the literature inspired by various events observable in nature, such as evolutionary phenomena, the actions of social creatures or agents, broad principles based on physical processes, the nature of chemical reactions, human behavior, superiority, and intelligence, intelligent behavior of plants, numerical techniques and mathematics programming procedure and its orientation. Nature-inspired metaheuristic algorithms have dominated the scientific literature and have become a widely used computing paradigm over the past two decades. Equilibrium Optimizer, popularly known as EO, is a population-based, nature-inspired meta-heuristics that belongs to the class of Physics based optimization algorithms, enthused by dynamic source and sink models with a physics foundation that are used to make educated guesses about equilibrium states. EO has achieved massive recognition, and there are quite a few changes made to existing EOs. This article gives a thorough review of EO and its variations. We started with 175 research articles published by several major publishers. Additionally, we discuss the strengths and weaknesses of the algorithms to help researchers find the variant that best suits their needs. The core optimization problems from numerous application areas using EO are also covered in the study, including image classification, scheduling problems, and many others. Lastly, this work recommends a few potential areas for EO research in the future.

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Denoising and segmentation in medical image analysis: A comprehensive review on machine learning and deep learning approaches

Kumar,

Priyadarshi

2024

Multimed Tools Appl

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Hybridization of Sine-Cosine Algorithm with K-Means for Pathology Image Clustering

Cited by 3 publications

References 24 publications

Semi-supervised spectral clustering using shared nearest neighbour for data with different shape and density

Semi-supervised spectral clustering using shared nearest neighbour for data with different shape and density

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Denoising and segmentation in medical image analysis: A comprehensive review on machine learning and deep learning approaches

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