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

How Data are Classified: Categorical Data

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Contingency Table

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Introduction to Test of Independence

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

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

Sort by

Same author

Bridging equity and sustainability: A heterogeneous analysis of poverty, inequality, and green growth's impact on environmental sustainability in BRI economies.

Journal of environmental management·2025

Same author

Corrigendum: An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique.

Frontiers in computational neuroscience·2025

Same author

An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique.

Frontiers in computational neuroscience·2024

Same author

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks.

Brain sciences·2023

Same author

Security Framework for Network-Based Manufacturing Systems with Personalized Customization: An Industry 4.0 Approach.

Sensors (Basel, Switzerland)·2023

Same author

Attention Aware Deep Learning Approaches for an Efficient Stress Classification Model.

Brain sciences·2023

Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026

Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026

Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026

Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026

Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026

Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 23, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Rough set based information theoretic approach for clustering uncertain categorical data.

Jamal Uddin¹, Rozaida Ghazali², Jemal H Abawajy³

¹Qurtuba University of Science & IT, Peshawar, Pakistan.

|May 13, 2022

Summary

This summary is machine-generated.

This study introduces a Rough Purity Approach (RPA) for clustering uncertain categorical data, improving efficiency and accuracy. The new method significantly enhances performance in large-scale data analysis across various domains.

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Related Experiment Videos

Last Updated: Sep 23, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Area of Science:

Data Mining and Machine Learning
Information Theory
Rough Set Theory

Background:

Real-world applications generate large categorical datasets with inherent uncertainty, posing challenges for traditional clustering algorithms.
Existing Rough Set Theory-based clustering methods for categorical data often struggle with accuracy, computational complexity, and generalizability.
Discovering non-trivial patterns in uncertain categorical data requires robust and efficient analytical techniques.

Purpose of the Study:

To propose a novel information theoretic approach, the Rough Purity Approach (RPA), for clustering uncertain categorical datasets.
To address the limitations of traditional Rough Set Theory-based categorical clustering techniques.
To achieve efficient clustering in terms of performance, generalizability, and computational complexity for uncertain categorical data.

Main Methods:

The Rough Purity Approach (RPA) leverages information-theoretic attribute purity within categorical-valued information systems.
Extensive experiments were conducted using a real-world Supplier Base Management (SBM) dataset and six benchmark UCI datasets.
The performance of RPA was rigorously compared against several state-of-the-art categorical data clustering techniques.

Main Results:

The Rough Purity Approach (RPA) demonstrated superior performance compared to baseline algorithms across key metrics.
Significant improvements were observed: 66.70% in time, 83.13% in iterations, 10.53% in purity, 14% in entropy, and 12.15% in accuracy.
RPA also showed enhanced Rough Accuracy of clusters, indicating its suitability for practical applications.

Conclusions:

Attribute purity in categorical-valued information systems provides a more effective basis for data clustering compared to existing techniques.
The Rough Purity Approach (RPA) is recommended for large-scale clustering tasks in diverse domains.
Further research can explore enhancements to the RPA for even greater performance gains.