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相关概念视频

Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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相关实验视频

Updated: Jul 3, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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快速多视图 - 图形集群

Ben Yang, Xuetao Zhang, Jinghan Wu

    IEEE transactions on neural networks and learning systems
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    概括
    此摘要是机器生成的。

    本研究介绍了一种离散的快速多视图图集群 (FMAGC) 模型,以克服大规模集群中基于图形的方法的局限性. 通过直接解决离散的图形切割问题而没有近似,FMAGC提高了有效性和效率.

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    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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    相关实验视频

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    科学领域:

    • 计算机科学 计算机科学
    • 数据科学数据科学数据科学
    • 机器学习 机器学习

    背景情况:

    • 基于图形的方法在大规模的多视图集群中面临着计算挑战.
    • 现有的加速方法往往接近离散问题,导致潜在的有效性和效率损失.
    • 图和指标学习方法已经取得了成功,但仍然依赖于近似策略.

    研究的目的:

    • 开发一个离散的多视图集群模型,避免近似和离散问题.
    • 提高大规模多视图集群的有效性和效率.
    • 为直接离散的图形切割问题解决提出一种新的优化策略.

    主要方法:

    • 建立了一个离散的快速多视图图集群 (FMAGC) 模型.
    • 每个视图都构建了图.
    • 一个离散的多视图图形切割问题是直接使用一个快速坐标下降基于优化策略与线性复杂性解决.

    主要成果:

    • 拟议的FMAGC模型有效地解决了近似和离散的局限性.
    • 坐标下降优化策略实现了线性复杂性,从而实现了高效的计算.
    • 实验表明,与各种数据集上的最先进方法相比,聚类的有效性和效率得到了提高.

    结论:

    • 通过直接处理离散的图形切割,FMAGC为大规模的多视图集群提供了一种优越的方法.
    • 该方法提供了有效性和计算效率之间的平衡.
    • 这项工作通过提供实用和高性能解决方案,推动了多视图集群领域的发展.