Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Scatter Plot

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:

Causes of Similarity-Dissimilarity Effect

Causes of Similarity-Dissimilarity Effect

The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society.

PloS one·2024

Same author

Drought-tolerant fungal microbes, <i>Aspergillus oryzae</i> and <i>Aspergillus fumigatus</i>, elevate physiohormonal and antioxidant responses of maize under drought stress.

Frontiers in microbiology·2024

Same author

Investigation of antibiotic resistance and biofilm formation ability of <i>Acinetobacter baumannii</i> isolated from urinary catheters.

Pakistan journal of medical sciences·2024

Same author

UV-Vis spectroscopy in the characterization and applications of smart microgels and metal nanoparticle-decorated smart microgels: a critical review.

RSC advances·2024

Same author

First investigation of high-performance FeS-based W<sub>18</sub>O<sub>49</sub> asymmetric supercapacitors operating at 1.6 V.

Nanoscale advances·2024

Same author

Establishment of callus induction and plantlet regeneration systems of Peucedanum Praeruptorum dunn based on the tissue culture method.

Plant methods·2024

Search research articles

Related Experiment Video

Updated: May 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

3D similarity-dissimilarity plot for high dimensional data visualization in the context of biomedical pattern

Muhammad Arif¹, Saleh Basalamah

¹College of Computer and Information Systems, Umm-Alqura University, Makkah, Kingdom of Saudi Arabia, mahamid@uqu.edu.sa.

Journal of Medical Systems

|April 16, 2013

Summary

High-dimensional biomedical data is hard to visualize. This study introduces a 3D similarity-dissimilarity plot to project complex feature spaces, aiding pattern classification and data point analysis.

Related Experiment Videos

Last Updated: May 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Biomedical data analysis
Machine learning
Data visualization

Background:

High dimensionality in biomedical datasets complicates feature space visualization.
Effective visualization is crucial for pattern classification and understanding data characteristics.

Purpose of the Study:

To propose a novel 3D similarity-dissimilarity plot for visualizing high-dimensional biomedical feature spaces.
To enable easier extraction of information for pattern classification tasks.
To facilitate the identification of good, bad, and outlier data points.

Main Methods:

Development of a 3D similarity-dissimilarity plot to reduce feature space dimensionality.
Visualization of data point distribution, class separation, and cluster compactness.
Introduction of the percentage of data points above the similarity-dissimilarity line (PAS) index.

Main Results:

The 3D plot effectively visualizes class separation and data point quality (good, bad, outliers).
Data point density in the plot provides insights into cluster compactness.
The PAS index offers a quantitative measure of data distribution relative to the similarity-dissimilarity line.

Conclusions:

The proposed 3D similarity-dissimilarity plot is an effective tool for analyzing high-dimensional biomedical data.
This visualization technique enhances pattern classification by revealing key feature space characteristics.
The method demonstrates utility across various synthetic and real-world biomedical datasets.