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Related Concept Videos

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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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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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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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

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After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Related Experiment Video

Updated: Oct 29, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Deep Clustering: A Comprehensive Survey.

Yazhou Ren, Jingyu Pu, Zhimeng Yang

    IEEE Transactions on Neural Networks and Learning Systems
    |July 4, 2024
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    Summary
    This summary is machine-generated.

    Deep clustering (DC) enhances machine learning by learning data representations with deep neural networks (DNNs). This survey categorizes DC methods by data sources, offering insights into complex clustering applications.

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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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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Cluster analysis is vital for machine learning and data mining.
    • Effective data representation is key for clustering algorithms.
    • Deep clustering (DC) leverages deep neural networks (DNNs) for improved representations.

    Purpose of the Study:

    • To provide a comprehensive survey of deep clustering methods.
    • To categorize DC approaches based on diverse data sources.
    • To address limitations of existing surveys focusing on single-view and network architectures.

    Main Methods:

    • Systematic classification of DC methods by data sources.
    • Distinguishing methods based on methodology, prior knowledge, and architecture.
    • Categorizing DC into traditional single-view, semi-supervised, multi-view, and transfer clustering.

    Main Results:

    • DC methods are categorized into four main types: traditional single-view DC, semi-supervised DC, deep multi-view clustering (MVC), and deep transfer clustering.
    • The survey highlights the importance of considering data sources for DC applications.
    • Analysis covers methodology, prior knowledge, and architectural differences across categories.

    Conclusions:

    • This survey offers a structured overview of deep clustering based on data sources.
    • It identifies key challenges and future research directions in the field.
    • The categorization provides a framework for understanding and advancing DC techniques.