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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.4K
Outliers and Influential Points01:08

Outliers and Influential Points

6.5K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
6.5K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.2K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.2K
What Are Outliers?01:12

What Are Outliers?

5.5K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
5.5K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

799
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
799
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.4K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Adaptive Charge Modulation Enables Focal, Selective Spinal Cord Stimulation.

bioRxiv : the preprint server for biology·2026
Same author

Effects of BSFL on growing-finishing pigs in growth performance, nutrient digestibility, blood profiles, and gas emission.

Journal of animal science and technology·2026
Same author

An ARMS-integrated high-resolution melting assay for reliable discrimination of the A1/A2 polymorphism in the bovine <i>CSN2</i> gene.

Food chemistry. Molecular sciences·2026
Same author

Effects of illite or bentonite on cytotoxicity, antibacterial and adsorption capacity in porcine intestinal epithelial cells.

Journal of animal science and technology·2026
Same author

Identifying the optimal ratios for replacing spray-dried plasma protein with hydrolyzed porcine intestinal protein in weaning pig.

Journal of animal science and technology·2026
Same author

Interactive effects of the feed-borne mycotoxin deoxynivalenol and a mixed-species Eimeria challenge on layer pullets during the transition to lay.

Poultry science·2026

Related Experiment Video

Updated: Mar 12, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K

Fast Outlier Detection Using a Grid-Based Algorithm.

Jihwan Lee1, Nam-Wook Cho2

  • 1Department of Industrial and Management Engineering, Hankuk University of Foreign Studies, Gyunggi-do, Republic of Korea.

Plos One
|November 11, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a grid-based algorithm to speed up Local Outlier Factor (LOF) calculations for large datasets. The grid-LOF method significantly reduces computation time while maintaining acceptable accuracy for outlier detection.

More Related Videos

Automated Detection and Analysis of Exocytosis
13:28

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

4.0K
Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

1.6K

Related Experiment Videos

Last Updated: Mar 12, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K
Automated Detection and Analysis of Exocytosis
13:28

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

4.0K
Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

1.6K

Area of Science:

  • Computer Science
  • Data Mining
  • Machine Learning

Background:

  • Outlier detection is crucial for identifying anomalous data points.
  • The Local Outlier Factor (LOF) algorithm is effective but computationally intensive.
  • High-dimensional and large-scale data pose challenges for traditional LOF application.

Purpose of the Study:

  • To propose a grid-based algorithm to enhance the computational efficiency of the LOF algorithm.
  • To reduce the time complexity of determining k-nearest neighbors in LOF.
  • To enable the application of LOF to large and high-dimensional datasets.

Main Methods:

  • Data space is partitioned into a grid of regions.
  • LOF values are calculated for each grid region.
  • The proposed grid-LOF algorithm's performance is evaluated through experiments.

Main Results:

  • The grid-based approach significantly reduces computation time compared to the original LOF.
  • A predictable and acceptable trade-off between speed and accuracy was observed.
  • The method was successfully applied to real-world transaction log data.

Conclusions:

  • Grid-LOF serves as an acceptable approximation of the original LOF for very large datasets.
  • The proposed methodology is effective for real-time outlier detection applications.