Agglomerative clustering using improved rough sets and its applications in cooperative object localization

Chen, Wei; Tang, Zhibo; Jiang, Xiaorong; Gao, Jing; Sun, Renke; Hashlan, Sanad

doi:10.1016/j.compeleceng.2013.04.008

Cited by 5 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In an agglomerative clustering model, the clustering initiates with individual collections of every data point [ 60 ]. AGNES has been extensively used in various medical domains [ 61 , 62 ], such as categorizing patients with severe aortic stenosis [ 63 ], and mapping molecular substructures [ 64 ]. However, AGNES has been ineffective in some problems since finding the nearest pair of clusters can be challenging when data is sparse and noisy [ 65 ].…”

Section: Methodsmentioning

confidence: 99%

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

et al. 2023

View full text Add to dashboard Cite

Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, productivity, and the world economy. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and further visualises and compares the results using selected dimensionality reduction methods that include principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for selected variants (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We find that the proposed framework can effectively distinguish between the major variants and has the potential to identify emerging variants in the future.

show abstract

Section: Methodsmentioning

confidence: 99%

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In an agglomerative clustering model, the clustering initiates with individual collections of every data point [55]. AGNES has been extensively used various medical domains [56,57], such as categorizing patients with severe aortic stenosis [58], and mapping molecular substructures [59]. However, AGNES has been ineffective in some problems since finding the nearest pair of clusters can be challenging when data is sparse and noisy [60].…”

Section: Agglomerative Clusteringmentioning

confidence: 99%

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Mingyue¹,

Vasan²,

Wilson³

et al. 2022

Preprint

View full text Add to dashboard Cite

Due to the rapid evolution of the SARS-CoV-2 (COVID-19) virus, a number of mutations emerged with variants such as Alpha, Gamma, Delta and Omicron which created massive impact to the world economy. Unsupervised machine learning methods have the ability to compresses, characterize and visualises unlabelled data. In this paper, we present a framework that utilizes unsupervised machine learning methods that includes combination of selected dimensional reduction and clustering methods to discriminate and visualise the associations with the major COVID-19 variants based on genome sequences. The framework utilises k-mer analysis for processing the genome (RNA) sequences and compares different dimensional reduction methods, that include principal component analysis (PCA), and t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Furthermore, the framework employs agglomerative hierarchical clustering methods and provides a visualisation using a dendogram. We find that the proposed framework can effectively distinguish the major variants and hence can be used for distinguishing emerging variants in the future.

show abstract

“…In the HSV and YCbCr color space, clustering can be used to find objects feature segmentation regions for objects difficult to be separated [10,34].…”

Section: Features Of Humans In Monitor Imagesmentioning

confidence: 99%

Skin color modeling for face detection and segmentation: a review and a new approach

Chen

Wang

Jiang

et al. 2014

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

Detecting skin-colored pixels, although seems a straightforward easy task, has proven to be quite a challenging task in images that are captured under complex unconstrained imaging conditions. Color segmentation is an important method of segmenting the body in images. In this paper, we first provides a review of skin color modeling research works for face detection. In particular, we focus on discussing the challenges of skin color modeling, detection, and segmentation, and then we present the skin color modeling and region-based skin segmentation. We also propose a new approach to analyze the hue characteristics of monitoring personnel in coal mine. We determine the skin color boundaries of the miners using the boundary detection method based on the response of visual hue. And then, we apply to determining hue area method using dual hue detector to segment skin color for miners. Experiments show that the method has excellent ability to segment skin regions of miners. We also summarize the most widely used methods for skin detection approaches and collecting their numerical evaluation results.

show abstract

Agglomerative clustering using improved rough sets and its applications in cooperative object localization

Cited by 5 publications

References 25 publications

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Skin color modeling for face detection and segmentation: a review and a new approach

Contact Info

Product

Resources

About