Fast K-Means Clustering for Very Large Datasets Based on MapReduce Combined with a New Cutting Method

Hieu, Duong Van; Meesad, Phayung

doi:10.1007/978-3-319-11680-8_23

Cited by 14 publications

(5 citation statements)

References 17 publications

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“…In an attempt to reduce difficulty in segmentation task, the k-means based clustering algorithms are used in the image segmentation. K-means based clustering is one of the famous algorithms as it had been implemented by several researchers [28][29][30][31][32]. In this study, various version of k-means clustering algorithms will be implemented.…”

Section: K-means Based Clustering Image Segmentationmentioning

confidence: 99%

Robust Segmentation of COVID-19 Chest X-Ray Images: Analysis of Variant k-Means Based Clustering Algorithms

Ooi Wei Herng,

Aimi Salihah Abdul Nasir,

Abdul Syafiq Abdull Sukor

2024

ARASET

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Computer aided diagnosis (CADx) become one the most famous method in diagnostic medical field due to the high reliability and efficiency. Recently, the coronavirus disease (COVID-19) has become severe global pandemic. Particularly, the Chest X-ray (CXR) imaging has become an essentiality in COVID-19 detection. As a result, the convergence of CADx technology with Chest X-ray analysis has achieved great efficiency in COVID-19 diagnosis. Therefore, the research value of CADx in COVID-19 diagnosis is exceptionally high. This study aims to evaluate different k-means based clustering algorithms and identifying the one with the highest overall accuracy. First of all, 150 COVID-19 CXR open-source images are acquired from Kaggle and Github. All the images will be unified into a same image size with 1000*1000 pixels and quality during the image pre-processing. Next, the resized images are enhanced by the Modified Global Contrast Stretching (MGCS) enhancement method to increase the quality of images. Then, the traditional k-means, k-medians, k-medoids and fast k-means clustering methods have been implemented in the image segmentation. At the same time, five different numbers <2, 4, 6, 8, 10> of clusters also tested out in this study. Lastly, all the segmented is proceeded to the segmentation performance based on sensitivity, specificity, accuracy, precision, recall and F-score. The result proves that the k-medoids clustering algorithm with 2 clusters archived the best overall segmentation performance as it obtained the highest sensitivity, accuracy, recall and F-score with 66.14%, 87.98%, 0.6614 and 0.7327.

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Section: K-means Based Clustering Image Segmentationmentioning

confidence: 99%

Robust Segmentation of COVID-19 Chest X-Ray Images: Analysis of Variant k-Means Based Clustering Algorithms

Ooi Wei Herng,

Aimi Salihah Abdul Nasir,

Abdul Syafiq Abdull Sukor

2024

ARASET

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“…Since all the features are numerical and the database is very large, the K-means clustering algorithm was used for its efficiency and convergence speed [34]. The silhouette metric [35] was used to evaluate the optimal number of clusters.…”

Section: Clustering Of Driving Stylesmentioning

confidence: 99%

Road Traffic Dynamic Pollutant Emissions Estimation: From Macroscopic Road Information to Microscopic Environmental Impact

2020

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Air pollution poses a major threat to health and climate, yet cities lack simple tools to quantify the costs and effects of their measures and assess those that are most effective in improving air quality. In this work, a complete modeling framework to estimate road traffic microscopic pollutant emissions from common macroscopic road and traffic information is proposed. A machine learning model to estimate driving behavior as a function of traffic conditions and road infrastructure is coupled with a physics-based microscopic emissions model. The up-scaling of the individual vehicle emissions to the traffic-level contribution is simply performed via a meta-model using both statistical vehicles fleet composition and traffic volume data. Validation results with real-world driving data show that: the driving behavior model is able to maintain an estimation error below 10% for relevant boundary parameter of the speed profiles (i.e., mean, initial, and final speed) on any road segment; the traffic microscopic emissions model is able to reduce the estimation error by more than 50% with respect to reference macroscopic models for major pollutants such as NOx and CO2. Such a high-resolution road traffic emissions model at the scale of every road segment in the network proves to be highly beneficial as a source for air quality models and as a monitoring tool for cities.

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“…Van Hieu D. and Meesad P. [15] has proposed an algorithm of KMeans for reducing execution time. They implemented the KMeans by cutting off the last iterations defined in K-Means.…”

Section: K-meansmentioning

confidence: 99%

Implementation of Parallelized K-means and K-Medoids++ Clustering Algorithms on Hadoop Map Reduce Framework

2019

IJITEE

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The electronic information from online newspapers, journals, conference proceedings website pages and emails are growing rapidly which are generating huge amount of data. Data grouping has been gotten impressive consideration in numerous applications. The size of data is raised exponentially due to the advancement of innovation and development, makes clustering of vast size of information, a challenging issue. With the end goal to manage the issue, numerous scientists endeavor to outline productive parallel clustering representations to be needed in algorithms of hadoop. In this paper, we show the implementation of parallelized K-Means and parallelized K-Medoids algorithms for clustering an large data objects file based on MapReduce for grouping huge information. The proposed algorithms combines initialization algorithm with Map Reduce framework to reduce the number of iterations and it can scale well with the commodity hardware as the efficient process for large dataset processing. The outcome of this paper shows the implementation of each algorithms.

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Fast K-Means Clustering for Very Large Datasets Based on MapReduce Combined with a New Cutting Method

Cited by 14 publications

References 17 publications

Robust Segmentation of COVID-19 Chest X-Ray Images: Analysis of Variant k-Means Based Clustering Algorithms

Robust Segmentation of COVID-19 Chest X-Ray Images: Analysis of Variant k-Means Based Clustering Algorithms

Road Traffic Dynamic Pollutant Emissions Estimation: From Macroscopic Road Information to Microscopic Environmental Impact

Implementation of Parallelized K-means and K-Medoids++ Clustering Algorithms on Hadoop Map Reduce Framework

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