The Silhouette Index and the K-Harmonic Means algorithm for Multispectral Satellite Images Clustering

Mahi, Habib; Farhi, Nezha; Labed, Kaouter; Benhamed, Dalila

doi:10.1109/icass.2018.8652068

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…Furthermore, we also focus on applying two widely used clustering algorithms, K-means and Hierarchical Clustering, to the context of the Cowhide SMEs Industry in Garut. Several studies underscore the critical importance of validating clustering results using the Silhouette score, as demonstrated in previous studies [24], [25], [26], [27].…”

Section: Related Workmentioning

confidence: 81%

Section: Related Workmentioning

confidence: 99%

“…Additionally, the Silhouette index has been explored in conjunction with the K-Harmonic Means method to group remote sensing datasets effectively [27]. This approach showcases the Silhouette index's ability to determine the correct number of clusters in scenarios with varying degrees of cluster overlap [27]. A study highlights the challenges of identifying compact and well-separated clusters within datasets, a task many clustering algorithms grapple with [28].…”

Section: Related Workmentioning

confidence: 99%

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Halal Supply Chain Risk using Unsupervised Learning Methods for Clustering Leather Industries

Kurniawan,

Lestari,

Mawardi

et al. 2024

IJCDS

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Cowhide plays a significant role in Indonesia's culinary and leather industries. It caters to the preferences of a predominantly Muslim population that emphasizes halal products. Regulatory authorities must understand its characteristics comprehensively to provide effective halal assurance to the diverse entities within Indonesia's leather industry. Traditional statistical methods for assessing halal compliance are inefficient due to the complexity and diversity of the leather industry's supply chain. This study addresses these challenges by employing unsupervised learning methods, specifically K-Means and Hierarchical clustering algorithms to analyze a dataset comprising 100 Cowhide Small and Medium Enterprises (SMEs) located in Garut Regency, West Java Province. This dataset includes 62 features that facilitate the clustering of these industries based on various halal risk factors. Experimental results indicate that the optimal number of clusters is four. The K-Means algorithm outperforms the Hierarchical clustering algorithm with a higher average silhouette score of 0.59 compared to 0.31. Furthermore, the K-Means algorithm demonstrates stability in clustering the data, making it a robust choice for this analysis. These clustering outcomes offer valuable insights into the SMEs operational characteristics and halal compliance risks, significantly enhancing the ability of regulatory authorities to implement effective halal assurance measures. Consequently, this study provides a robust framework for improving halal certification processes and aiding risk management within Indonesia's leather industry.

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Section: Related Workmentioning

confidence: 81%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Halal Supply Chain Risk using Unsupervised Learning Methods for Clustering Leather Industries

Kurniawan,

Lestari,

Mawardi

et al. 2024

IJCDS

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“…Rand and Adjusted Rand index. The Silhouette index (Mahi, et al, 2018) is one of many cluster indices that can be used to find the number of clusters. The highest value of the average of the Silhouette index si indicates a suitable number of clusters.…”

Section: Gkaoptmentioning

confidence: 99%

“…GKA is a single objective optimization method that use only one fitness function. DB index (Mahi, et al, 2018) is used as their fitness function to measure the validity of the clustering algorithm. This index measured the similarity between the clusters (how separated and compact are the clusters).…”

Section: Gkaoptmentioning

confidence: 99%

Performance Evaluation of Novel Selection Processes Through Hybridization of K-Means Clustering and Genetic Algorithm

Haq¹

2019

Appl. Ecol. Env. Res.

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K-means clustering combined with genetic algorithm (GA) techniques are used to improve the accuracy of estimation process and to minimize computational effort for solving nonlinear optimization problems. The main purpose of K-means clustering is to exhibit faster convergence which turns into quick evolution. This paper focuses on newly proposed cluster based GA selection techniques for solving unconstrained optimization problems. The K-means cluster based genetic algorithm (GKA) selection techniques comprise of four major stages: clustering, membership probability indexing, fitness evaluation and selection. The hybridization of genetic algorithm and clustering will effectively cater the problem of population diversity and selection pressure. There are two types of GKA selection techniques that are examined, the first selection technique (GKAF) includes two proposed selection operators which are linked with a fixed number of clusters while the second technique (GKAopt) is based on the optimum number of clusters. The main focus of these new selection techniques is to preserve population diversity as well as to avoid local optima. The performance of each technique is evaluated through eleven well known benchmark functions. On the whole, the novel cluster based selection techniques are demonstrated to be extremely efficient and effective for achieving optimum solutions which are verified by simulated results.

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K-Means and BIRCH: A Comparative Analysis Study

Tomar

Sharma

2022

Lecture Notes in Networks and Systems

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The Silhouette Index and the K-Harmonic Means algorithm for Multispectral Satellite Images Clustering

Cited by 13 publications

References 15 publications

Halal Supply Chain Risk using Unsupervised Learning Methods for Clustering Leather Industries

Halal Supply Chain Risk using Unsupervised Learning Methods for Clustering Leather Industries

Performance Evaluation of Novel Selection Processes Through Hybridization of K-Means Clustering and Genetic Algorithm

K-Means and BIRCH: A Comparative Analysis Study

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