Finding the number of clusters in ordered dissimilarities

Sledge, Isaac J.; Havens, Timothy C.; Huband, Jacalyn M.; Bezdek, James C.; Keller, James M.

doi:10.1007/s00500-009-0421-5

Cited by 19 publications

(4 citation statements)

References 32 publications

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“…The clustering tendency assessment problem consists of estimating whether there are (natural) clusters in the data and their quantity. The VAT family of algorithms [27], [28], [29], [30], [31] are among the few oriented to this problem (VAT stands for visual assessment of (cluster) tendency). The general idea is to reorder a pairwise dissimilarity matrix and then display it as a dissimilarity image, where possible clusters in the data can be identified as dark blocks aligned along the main diagonal.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Analysis of cough sound measurements including COVID-19 positive cases: A machine learning characterization

Valdés

Cohen-McFarlane

et al. 2021

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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With the rise of COVID-19, remote monitoring tools that can monitor or possibly identify respiratory conditions would be very beneficial. One of the most common symptoms of respiratory disease is cough, whose characteristics can provide medical insight to health care professionals when identifying the underlying cause (e.g. virus, bacteria, or acute/chronic condition) [13]. Cough characteristics are typically explored by a physician during an appointment by 1) asking the patient to describe the characteristics of the cough, e.g. frequency and productivity, 2) listening to involuntary coughs during the encounter, 3) listening to the lungs of the patient during deep breathing and 4) potentially asking the patient to produce a voluntary cough. However, voluntary coughs may have different characteristics when compared to an involuntary cough [14]. Self-report measures are well known to be inconsistent, which is especially true in the older adult population.The automatic characterization of cough based on some of the current medical ontology (wet cough, dry cough, whooping cough) may prove beneficial as an additional source of information for physicians when making medical decisions. Furthermore, characterization of specific underlying condition associated with a particular cough (e.g. COVID-19) may have implication in disease identification, monitoring and contact tracing in public areas.Studies have indicated that cough caused by COVID-19 is likely to have distinct latent features. In [15], the authors analyzed the pathomorphological changes caused by the COVID-19 in the respiratory system from the studies examining Xrays and CT scans of COVID-19 patients. Their study also included the autopsy report studies of deceased patients. Their findings suggest that cough sound signatures with COVID-19 are likely to have some idiosyncrasies stemming from the distinct underlying pathomorphological alterations. In an-Abstract-Remote monitoring and measurement are valuable tools for medical applications and they are particularly important in the context of pandemic outbreaks, like the current COVID-19. This paper presents an analysis of sound measurements of cough events from the point of view of their predictive content with respect to identification of different types of cough, including positive COVID-19 cases. The data consisted of a collection of audio samples collected from different sources including dry, wet, whooping and COVID-19 coughs. Unsupervised and supervised machine learning techniques were used to reveal the underlying structure of the data, described by dissimilarity spaces constructed from pair-wise dynamic time warping measures derived from the original sound measurements. Intrinsic dimensionality, nonlinear mappings to low-dimensional spaces and visual cluster assessment techniques allowed a representation of the cough types distribution. Supervised classification t echniques w ere u sed to obtain models identifying cough classes and high performance classifiers w ere o btained for m ost o f t hem, i ncludi...

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Analysis of cough sound measurements including COVID-19 positive cases: A machine learning characterization

Valdés

Cohen-McFarlane

et al. 2021

2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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“…These threshold plots were then combined to generate a clustering score for the particular data set. Later works by Sledge et al [52], [53] automatically (algorithmically) determined , k the number of clusters from the VAT image. They experimented on a variety of ineffective techniques before proposing a cluster count extraction (CCE) algorithm, which implemented frequency domain correlation and feature recognition to count the number of clusters automatically.…”

Section: Automatic Assessment Based On Imagementioning

confidence: 99%

Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of Algorithms

Kumar

Bezdek

2020

IEEE Syst. Man Cybern. Mag.

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“…There are also methods that read the VAT image to determine cluster tendency automatically [24,28]. Researchers have also extended VAT to answer the cluster validity question [4,10,13].…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms include, but are not limited to, sVATscalable VAT [11], bigVAT [15], reVAT-revised VAT [14], coVAT [2], VCV-Visual Cluster Validity [4,10,13], CLODD-CLustering in Ordered Dissimilarity Data [12], CCV-Correlation Cluster Validity [21], and CCE-Cluster Count Extraction [24,28].…”

mentioning

confidence: 99%

Is VAT really single linkage in disguise?

Havens

Bezdek

Keller

et al. 2009

Ann Math Artif Intell

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This paper addresses the relationship between the Visual Assessment of cluster Tendency (VAT) algorithm and single linkage hierarchical clustering. We present an analytical comparison of the two algorithms in conjunction with numerical examples to show that VAT reordering of dissimilarity data is directly related to the clusters produced by single linkage hierarchical clustering. This analysis is important to understanding the underlying theory of VAT and, more generally, other algorithms that are based on VAT-ordered dissimilarity data.

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Finding the number of clusters in ordered dissimilarities

Cited by 19 publications

References 32 publications

Analysis of cough sound measurements including COVID-19 positive cases: A machine learning characterization

Analysis of cough sound measurements including COVID-19 positive cases: A machine learning characterization

Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of Algorithms

Is VAT really single linkage in disguise?

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