CLAMS:用于估计视觉集群中的感知变量的一种集群模糊度量
IEEE transactions on visualization and computer graphics
|November 3, 2023
概括
这项研究引入了CLAMS,一种用于量化分散图中的集群模糊性的新方法. CLAMS准确地预测人类如何感知视觉集群,提高数据分析可靠性.
科学领域:
- 数据可视化 数据可视化
- 人与计算机的交互
- 感知科学 感知科学
背景情况:
- 在散布图中进行视觉聚类对于数据分析至关重要,但受到个人感知变化的影响.
- 模两可的集群界限和个体差异导致可靠的视觉集群结果不可靠.
- 现有的方法缺乏系统的方法来评估视觉聚类中的感知变异性.
研究的目的:
- 系统地研究和量化视觉集群中的感知变化,称为集群模糊性.
- 引入CLAMS,这是一个数据驱动的视觉质量测量工具,用于预测单色散射图中的集群模糊性.
- 提供一种可靠的方法来评估视觉集群的模糊性.
主要方法:
- 进行了一项定性研究,以确定影响视觉集群分离的因素 (例如近距离,大小).
- 开发了一个回归模块,以估计集群对之间的人类判断的分离性.
- 汇总的双相分离度得分,以预测使用CLAMS.的整体集群模两可.
主要成果:
- CLAMS准确地预测了基准真理集群模糊性,超过了现有的集群技术.
- 在评估集群模糊性方面,CLAMS的表现与人类注释者相当.
- 该测量有效量化视觉聚类任务中的感知变量.
结论:
- CLAMS提供了一种可靠和有效的方法来评估分散图中的集群模糊性.
- 开发的测量可以用来优化和基准数据挖掘技术.
- 这项工作提高了依赖视觉聚类的数据分析的可靠性.
更多相关视频
07:34Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
Published on: June 3, 2013
17.4K
12:27Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
7.0K
相关概念视频
Variability: Analysis
144
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...
The range is a simple measure of variability, indicating the difference between the highest and...
144
Perceptual Constancy
411
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
411
What is Variation?
11.8K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
11.8K
Cluster Sampling Method
11.9K
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...
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...
11.9K
Depth Perception and Spatial Vision
678
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
678
