Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
Reducing Line Loss01:18

Reducing Line Loss

144
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
144
Weighted Mean00:57

Weighted Mean

4.9K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
4.9K
Structural Classification of Joints01:20

Structural Classification of Joints

3.2K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.2K
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

46
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
46
Variability: Analysis01:11

Variability: Analysis

126
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
126

You might also read

Related Articles

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

Sort by
Same author

A catalogue of missense and nonsense mutation abundances for the U.S. cancer patient population.

medRxiv : the preprint server for health sciences·2026
Same author

Evaluation and management of DMD gene copy number variations detected by prenatal SNP-array testing.

BMC medical genomics·2026
Same author

Hairy cell leukemia: a chronic B-cell lymphoma with unique clinicopathological features and unresolved molecular mechanisms.

Blood advances·2026
Same author

Hairy cell leukemia: a chronic B-cell lymphoma with unique clinicopathological features and unresolved molecular mechanisms.

Blood advances·2025
Same author

Dual-modification of biochar <i>via</i> Fenton oxidation and <i>in situ</i> α-FeOOH synthesis for enhanced Cu(ii) removal: experimental investigation and theoretical calculation analysis.

RSC advances·2025
Same author

Ultrasound and genetic findings in a case series of fetuses presenting vertebral defects.

BMC pregnancy and childbirth·2025

Related Experiment Video

Updated: Jun 5, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

Unsupervised attribute reduction based on variable precision weighted neighborhood dependency.

Yi Li1, Benwen Zhang1, Hongming Mo1

  • 1Institute of Computer Application Research, Sichuan Minzu College, Kangding 626001, China.

Iscience
|December 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an unsupervised attribute reduction (UAR) method using variable precision weighted neighborhood dependency (VPWND). The new UAR_VPWND algorithm effectively reduces attributes while maintaining or enhancing clustering performance.

Keywords:
Artificial intelligenceComputational mathematicsComputer science

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

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

Published on: February 15, 2017

6.9K

Related Experiment Videos

Last Updated: Jun 5, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

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

Published on: February 15, 2017

6.9K

Area of Science:

  • Data Mining
  • Machine Learning
  • Rough Set Theory

Background:

  • Neighborhood rough set (NRS) methods are effective for attribute reduction (AR).
  • Existing NRS-based AR methods are often supervised or semi-supervised, limiting their use with unlabeled data.
  • Current NRS approaches do not consider sample distribution, potentially losing information.

Purpose of the Study:

  • To propose a novel unsupervised attribute reduction (UAR) strategy.
  • To address the limitations of existing NRS-based AR methods for unlabeled data.
  • To improve information preservation during attribute reduction.

Main Methods:

  • Developed an unsupervised attribute reduction (UAR) strategy named UAR_VPWND.
  • Utilized variable precision weighted neighborhood dependency (VPWND) for data granulation.
  • Compared UAR_VPWND against classical UAR algorithms on public datasets.

Main Results:

  • The UAR_VPWND algorithm successfully performed unsupervised attribute reduction.
  • UAR_VPWND selected fewer attributes compared to existing methods.
  • The reduced attribute sets maintained or improved the performance of clustering algorithms.

Conclusions:

  • The proposed UAR_VPWND strategy is effective for attribute reduction without decision information.
  • This method offers a promising approach for handling unlabeled data in attribute reduction tasks.
  • UAR_VPWND demonstrates superior or comparable performance in clustering tasks with fewer attributes.