A novel data visualization method for the effective assessment of cluster tendency through the dark blocks image pattern analysis

Prasad, K. Rajendra; Kamatam, Govardhan Reddy; Bala, Myneni Madhu; Reddy, N. Ramanjaneya

doi:10.1016/j.micpro.2022.104625

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…In the study of Prasad et al, existing data visualization methods have been enhanced by spectral modeling to overcome the problem of cluster bias on non-CS datasets, which efficiently recognizes the spectral features of non-CS datasets and cluster patterns [18]. New visualization techniques are used not only for environmental data, but also for other types of data such as satellite information, X-ray spectra processing or big data [19][20][21][22]. For this purpose, a software platform was developed with an integration in the form of two measurement stations and satellite information in a unified view to process publicly available data from various sources.…”

Section: B Related Workmentioning

confidence: 99%

Actionable Data Visualization for Air Quality Data in the Istanbul Location

MENGÜŞ

Daş

2022

Balkan Journal of Electrical and Computer Engineering

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Air pollution is increasing day by day due to the increasing population, urbanization, and industrial development. In our country, the amounts of pollutants in the air are recorded every day at different points. These recorded data continue to be collected in an increasing amount day by day. Information overload, which renders the data meaningless, complicates the interpretation of these data. One of the ways to solve this problem is to visualize curves and trends in measured pollution concentrations over time. In this study, using the data provided by the continuous monitoring center of the Turkey Ministry of Environment, Urbanization and Climate Change, visualization of different pollutants in the air was provided. Scatter plots, line scatter plots, and bar plots were used as data visualization. Data visualization makes it easy for non-experts to estimate air quality information from the concentration profiles displayed.

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Section: B Related Workmentioning

confidence: 99%

Actionable Data Visualization for Air Quality Data in the Istanbul Location

MENGÜŞ

Daş

2022

Balkan Journal of Electrical and Computer Engineering

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Implementation of an adaptive E-learning platform with facial emotion recognition

Bala¹,

Akkineni

Sirivella³

et al. 2023

Microsyst Technol

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Visual Assessment of Cluster Tendency with Variations of Distance Measures

Shkaberina

Rezova²,

Tovbis³

et al. 2022

Algorithms

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Finding the cluster structure is essential for analyzing self-organized networking structures, such as social networks. In such problems, a wide variety of distance measures can be used. Common clustering methods often require the number of clusters to be explicitly indicated before starting the process of clustering. A preliminary step to clustering is deciding, firstly, whether the data contain any clusters and, secondly, how many clusters the dataset contains. To highlight the internal structure of data, several methods for visual assessment of clustering tendency (VAT family of methods) have been developed. The vast majority of these methods use the Euclidean distance or cosine similarity measure. In our study, we modified the VAT and iVAT algorithms for visual assessment of the clustering tendency with a wide variety of distance measures. We compared the results of our algorithms obtained from both samples from repositories and data from applied problems.

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A novel data visualization method for the effective assessment of cluster tendency through the dark blocks image pattern analysis

Cited by 3 publications

References 15 publications

Actionable Data Visualization for Air Quality Data in the Istanbul Location

Actionable Data Visualization for Air Quality Data in the Istanbul Location

Implementation of an adaptive E-learning platform with facial emotion recognition

Visual Assessment of Cluster Tendency with Variations of Distance Measures

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