Clustering Algorithms
Clustering Algorithms - Related Videos
10:37
Spatial Separation of Molecular Conformers and Clusters
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Cited by 3 •
2014
We present a technique that allows the spatial separation of different conformers or clusters present in a molecular beam. An electrostatic deflector is used to separate species by their mass-to-dipole moment ratio, leading to the production of gas-phase ensembles of a single conformer or cluster stoichiometry.
01:12
Trial and Error and Algorithm
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2025
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light bulb,...
01:20
Cluster Sampling Method
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2023
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...
01:45
Vesicular Tubular Clusters
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2023
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...
12:29
Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
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Cited by 11 •
2017
Two image analysis algorithms, "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics" were created, to automatically quantify nine morphological features of the Drosophila neuromuscular junction (NMJ).
FAQs
The selected similarity or distance measure determines which observations appear related and therefore strongly shapes the resulting groups. In biological datasets, this means clusters reflect the characteristics emphasized by that comparison, such as resemblance among gene expression profiles or cells. Interpreting the measure is essential because the clusters are meaningful only in relation to the features being compared.
K-means begins with centroids, which represent the centers of candidate groups, and assigns each observation to the nearest centroid. It then iteratively places observations according to these nearest-center relationships as the clustering arrangement develops. This approach is useful when researchers want groups organized around central profiles, such as collections of genes or cells with similar measurements.
Hierarchical clustering builds a nested tree of relationships rather than only separating observations around centroids. The tree can show relationships among groups at multiple levels, allowing researchers to examine broad divisions alongside finer subdivisions. This perspective is valuable when biological samples may contain layered similarities, such as related species, populations, or expression profiles.
High-dimensional experiments contain many measured characteristics, making overall structure difficult to interpret directly. Clustering algorithms organize observations according to shared patterns, helping expose relationships that may not be obvious in the original data. In biology, this supports examination of complex gene expression and single-cell datasets, where grouping can make underlying cellular or molecular structure easier to investigate.
Researchers can compare gene expression profiles and place genes with similar patterns into groups, helping identify coordinated biological behavior. The same strategy can classify cells in single-cell datasets according to shared characteristics. These groupings provide an organized view of molecular or cellular diversity and can guide subsequent interpretation of high-dimensional experiments.
Clustering can identify microbial or ecological communities by grouping observations that share measured characteristics. It can also compare related species or populations, revealing patterns of resemblance across those biological units. These applications extend the method beyond gene and cell analysis, making it useful for examining structure in communities and relationships among organisms.
The resulting groups can support biomarker discovery, hypothesis generation, and interpretation of complex biological experiments. A cluster may reveal a pattern worth testing rather than establish a final biological explanation. Consequently, researchers use clustering outcomes to organize observations, identify potentially informative features, and develop focused questions for further biological investigation.