Efficient Fuzzy Image Stretching for Automatic Ganglion Cyst Extraction Using Fuzzy C-Means Quantization

Song, Doo Heon; Kim, Kwang-Baek; Park, Hyun Jun

doi:10.3390/app112412094

Cited by 3 publications

(2 citation statements)

References 22 publications

(35 reference statements)

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“…Equation (7) indicates that the degree of membership of an object i to a cluster j must be between 0 and 1. Equation (8) defines that the sum of the degrees of membership of an object i to different clusters must be equal to 1. Equation (9) indicates that the sum of all the degrees of membership in a cluster must be greater than 0 and less than n; that is, there must be no empty clusters and only one cluster.…”

Section: Fcm Algorithmmentioning

confidence: 99%

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Hybrid Fuzzy C-Means Clustering Algorithm Oriented to Big Data Realms

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Roblero-Aguilar

Almanza-Ortega

et al. 2022

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A hybrid variant of the Fuzzy C-Means and K-Means algorithms is proposed to solve large datasets such as those presented in Big Data. The Fuzzy C-Means algorithm is sensitive to the initial values of the membership matrix. Therefore, a special configuration of the matrix can accelerate the convergence of the algorithm. In this sense, a new approach is proposed, which we call Hybrid OK-Means Fuzzy C-Means (HOFCM), and it optimizes the values of the membership matrix parameter. This approach consists of three steps: (a) generate a set of n solutions of an x dataset, applying a variant of the K-Means algorithm; (b) select the best solution as the basis for generating the optimized membership matrix; (c) resolve the x dataset with Fuzzy C-Means. The experimental results with four real datasets and one synthetic dataset show that HOFCM reduces the time by up to 93.94% compared to the average time of the standard Fuzzy C-Means. It is highlighted that the quality of the solution was reduced by 2.51% in the worst case.

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Section: Fcm Algorithmmentioning

confidence: 99%

“…The above description corresponds to a hard or conventional clustering type, often related to the K-Means algorithm [5]. However, in many domains, each object needs to belong to two or more groups with different degrees of membership [6][7][8]. The algorithm that allows for the above is associated with Fuzzy C-Means (FCM).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fuzzy C-Means Clustering Algorithm Oriented to Big Data Realms

Pérez-Ortega

Roblero-Aguilar

Almanza-Ortega

et al. 2022

Axioms

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Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning

Liao,

Wang,

Wang

et al. 2024

Ultrasound in Medicine & Biology

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POFCM: A Parallel Fuzzy Clustering Algorithm for Large Datasets

et al. 2023

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Clustering algorithms have proven to be a useful tool to extract knowledge and support decision making by processing large volumes of data. Hard and fuzzy clustering algorithms have been used successfully to identify patterns and trends in many areas, such as finance, healthcare, and marketing. However, these algorithms significantly increase their solution time as the size of the datasets to be solved increase, making their use unfeasible. In this sense, the parallel processing of algorithms has proven to be an efficient alternative to reduce their solution time. It has been established that the parallel implementation of algorithms requires its redesign to optimise the hardware resources of the platform that will be used. In this article, we propose a new parallel implementation of the Hybrid OK-Means Fuzzy C-Means (HOFCM) algorithm, which is an efficient variant of Fuzzy C-Means, in OpenMP. An advantage of using OpenMP is its scalability. The efficiency of the implementation is compared against the HOFCM algorithm. The experimental results of processing large real and synthetic datasets show that our implementation tends to more efficiently solve instances with a large number of clusters and dimensions. Additionally, the implementation shows excellent results concerning speedup and parallel efficiency metrics. Our main contribution is a Fuzzy clustering algorithm for large datasets that is scalable and not limited to a specific domain.

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Efficient Fuzzy Image Stretching for Automatic Ganglion Cyst Extraction Using Fuzzy C-Means Quantization

Cited by 3 publications

References 22 publications

Hybrid Fuzzy C-Means Clustering Algorithm Oriented to Big Data Realms

Hybrid Fuzzy C-Means Clustering Algorithm Oriented to Big Data Realms

Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning

POFCM: A Parallel Fuzzy Clustering Algorithm for Large Datasets

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