Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
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...
12.0K
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

76
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...
76
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.3K
Factorial Design02:01

Factorial Design

13.1K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

An Indicator Based on Spatial Coordinate Information for Assessing the Capability for Dynamic Machining Performance of Five-Axis Flank Milling.

Sensors (Basel, Switzerland)·2024
Same author

The effect of lactic acid bacteria on cocoa bean fermentation.

International journal of food microbiology·2015
Same author

The performance of a combined nitritation-anammox reactor treating anaerobic digestion supernatant under various C/N ratios.

Journal of environmental sciences (China)·2015
Same author

Cytotoxicity profile of novel sterically hindered platinum(II) complexes with (1R,2R)-N(1),N(2)-dibutyl-1,2-diaminocyclohexane.

European journal of medicinal chemistry·2015
Same author

Functional Proteomics Study Reveals SUMOylation of TFII-I is Involved in Liver Cancer Cell Proliferation.

Journal of proteome research·2015
Same author

Bisulfite pretreatment changes the structure and properties of oil palm empty fruit bunch to improve enzymatic hydrolysis and bioethanol production.

Biotechnology journal·2015

相关实验视频

Updated: Jul 15, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K

一种基于相邻网格搜索的新聚类方法.

Zhimeng Li1, Wen Zhong1, Weiwen Liao1

  • 1School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, China.

Entropy (Basel, Switzerland)
|September 28, 2023
PubMed
概括

一种新的聚类方法,CAGS,使用自适应网格和相邻搜索来分析数据结构. CAGS有效地识别了集群和噪声,在各种数据集上表现优于现有的方法.

关键词:
聚类集群是指聚类的聚类.德诺瓦斯 (Denoise) 是一个基于网格的方法.高维度是指高维度的东西.这是一个大规模的大规模.没有监督的学习学习.

更多相关视频

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

7.0K
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

1.1K

相关实验视频

Last Updated: Jul 15, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K
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

7.0K
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

1.1K

科学领域:

  • 数据科学数据科学数据科学
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 聚类对于数据分析至关重要,用于图像细分,对象识别和信息检索.
  • 由于数据分析的广泛应用,强大的集群方法至关重要.
  • 现有的基于网格的集群方法面临着高维度和细胞数量增加的挑战.

研究的目的:

  • 提出一种新的集群方法,即通过相邻网格搜索 (CAGS) 进行集群,以进行可靠的数据分析.
  • 为了解决传统的网格集群的局限性,例如具有维度的细胞的急剧增加.
  • 开发一种能够自动识别噪音和集群光环的方法.

主要方法:

  • CAGS采用两步策略:自适应电网空间建设和相邻电网搜索.
  • 第一步将数据量化为一个多维网格,根据网格密度区分噪声和光环.
  • 第二步使用双阶段穿越来识别集群核心,有效处理任意形状并隐藏光环点.

主要成果:

  • 根据电网密度,CAGS成功地区分了噪音和集群光环.
  • 适应性网格生成过程减轻了具有数据尺寸的单元的急剧增加.
  • 实验结果表明,CAGS在各种数据集上优于最先进的方法,包括杂,大规模,高维,任意形状,不同密度和重叠类.

结论:

  • CAGS提供了一个强大而有效的解决方案,用于集群各种数据集.
  • 该方法自动识别集群的数量,并处理复杂的数据结构.
  • 在数据分析和机器学习任务中,CAGS显示出广泛应用的巨大潜力.