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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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Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

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The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
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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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Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

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Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
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Related Experiment Video

Updated: Dec 31, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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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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Deep Clustering: On the Link Between Discriminative Models and K-Means.

Mohammed Jabi, Marco Pedersoli, Amar Mitiche

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 4, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Recent deep clustering research shows discriminative models are equivalent to K-means. This finding leads to a new soft, regularized deep K-means algorithm with competitive performance on image clustering tasks.

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    Area of Science:

    • Machine Learning
    • Deep Learning
    • Unsupervised Learning

    Background:

    • Discriminative models, commonly used in deep clustering, often outperform generative approaches.
    • These models typically employ logistic regression posteriors and parameter regularization, similar to supervised learning.

    Purpose of the Study:

    • To demonstrate the equivalence between recent discriminative deep clustering models and K-means.
    • To introduce a novel deep K-means algorithm based on theoretical findings.

    Main Methods:

    • Theoretical analysis connecting discriminative objective functions (mutual information maximization) to K-means loss.
    • Utilizing an approximate alternating direction method (ADM) for optimization.

    Main Results:

    • Proved that L2 regularized mutual information maximization is equivalent to minimizing a soft, regularized K-means loss for logistic regression posteriors.
    • Developed a new soft and regularized deep K-means algorithm.

    Conclusions:

    • State-of-the-art discriminative deep clustering models can be viewed as variants of K-means.
    • The proposed deep K-means algorithm achieves competitive results on image clustering benchmarks.