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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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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
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Polymers: Molecular Weight Distribution01:10

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For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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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...
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Related Experiment Video

Updated: Jul 23, 2025

Spatial Separation of Molecular Conformers and Clusters
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A Clustering Algorithm for Polygonal Data Applied to Scientific Journal Profiles.

Wagner J F Silva, Pedro J C Souza, Renata M C R Souza

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 19, 2023
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    Summary
    This summary is machine-generated.

    Researchers can now profile scientific journals using a novel dynamic clustering algorithm for symbolic data. This method reveals key variables, like abstract complexity, for understanding journal characteristics.

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    Area of Science:

    • Bibliometrics
    • Data Science
    • Information Science

    Background:

    • Researchers require better tools to understand scientific journals amidst vast publication volumes.
    • Existing methods offer limited insight into journal characteristics and variability.

    Purpose of the Study:

    • To introduce a novel dynamical clustering algorithm for symbolic polygonal data.
    • To apply this algorithm for constructing comprehensive scientific journal profiles.
    • To develop interpretation indices for enhanced understanding of clustering results.

    Main Methods:

    • Development of a dynamical clustering algorithm tailored for symbolic polygonal data.
    • Application of the algorithm to create detailed scientific journal profiles.
    • Creation of cluster and partition interpretation indices for polygonal data analysis.

    Main Results:

    • The algorithm successfully builds profiles of scientific journals.
    • Symbolic polygonal data effectively represents summarized datasets with variability.
    • Interpretation indices provide valuable insights into clustering outcomes.
    • The frequency of complex words in abstracts emerged as a key variable for journal profiling.

    Conclusions:

    • The developed dynamical clustering approach offers a powerful method for scientific journal profiling.
    • Symbolic data representation and analysis are effective for understanding complex datasets.
    • Abstract linguistic complexity is a significant factor in defining journal identity.