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

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...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

Random Sampling Method

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...

The Representativeness Heuristic

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Random Variables

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...

You might also read

Related Articles

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

Sort by

Same author

Dental Care Needs and Treatment Priorities in a Homeless Population in Rome: An Observational Study.

Dentistry journal·2026

Same author

Experiences and theory reveal a decrease in antimicrobial blue light killing efficiency as biofilm grows in a Pseudomonas aeruginosa model.

Scientific reports·2026

Same author

More Than Just Steps: Harmonious Passion, Self-Confidence, and Emotion-Related Feelings in Female Amateur Dancers.

Journal of dance medicine & science : official publication of the International Association for Dance Medicine & Science·2025

Same author

Multi-criteria decision analysis for monitoring and evaluating soil erosion risk in forest fire-affected areas.

Journal of environmental management·2024

Same author

GAFchromic EBT film lateral resolution and contrast reproduction in the UV-blue range.

Scientific reports·2024

Same author

The safe use of lasers in biomedicine: Principles of laser-matter interaction.

Journal of public health research·2023

Search research articles

Related Experiment Video

Updated: May 23, 2026

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

Consensus clustering based on a new Probabilistic Rand Index with application to subtopic retrieval.

Claudio Carpineto¹, Giovanni Romano

¹Fondazione Ugo Bordoni, Viale del Policlinico 147, 00161 Roma, Italy.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|March 28, 2012

Summary

We developed the Probabilistic Rand Index (PRI) to measure partition similarity, weighting chance agreements and disagreements. Consensus clustering using PRI and a stochastic algorithm significantly improved results in various applications, including subtopic retrieval.

Related Experiment Videos

Last Updated: May 23, 2026

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

Area of Science:

Data Mining
Machine Learning
Statistical Analysis

Background:

Measuring the similarity between data partitions is crucial in various analytical tasks.
The traditional Rand Index (RI) offers a measure but lacks probabilistic weighting for chance occurrences.
Consensus clustering aims to combine multiple partitions into a single, more robust representation.

Purpose of the Study:

To introduce a novel Probabilistic Rand Index (PRI) that accounts for chance in partition similarity.
To frame consensus clustering as an optimization problem solvable by maximizing PRI.
To evaluate the performance of PRI-based consensus clustering against existing methods.

Main Methods:

Developed the Probabilistic Rand Index (PRI) by incorporating probability weighting for object-pair agreements and disagreements.
Formulated consensus clustering as an optimization task to maximize the PRI between a target partition and a set of input partitions.
Employed a simple and efficient stochastic optimization algorithm to solve the PRI optimization problem.

Main Results:

Demonstrated significant performance gains using PRI-based consensus clustering compared to input partitions.
Showcased superior results when compared to existing related consensus clustering methods.
Validated the effectiveness through diverse applications, including a novel application in subtopic retrieval.

Conclusions:

The Probabilistic Rand Index (PRI) provides a more nuanced and accurate measure of partition similarity.
PRI-based consensus clustering offers a powerful approach for improving data partitioning and analysis.
This method shows promise for enhancing tasks like subtopic retrieval and other data mining applications.