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  2. Similarity measure - Wikipedia

    en.wikipedia.org/wiki/Similarity_measure

    A similarity measure can take many different forms depending on the type of data being clustered and the specific problem being solved. One of the most commonly used similarity measures is the Euclidean distance, which is used in many clustering techniques including K-means clustering and Hierarchical clustering. The Euclidean distance is a ...

  3. Structural similarity index measure - Wikipedia

    en.wikipedia.org/wiki/Structural_similarity...

    The SSIM index is a full reference metric; in other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free image as reference. SSIM is a perception -based model that considers image degradation as perceived change in structural information, while also incorporating important perceptual ...

  4. Dice-Sørensen coefficient - Wikipedia

    en.wikipedia.org/wiki/Dice-Sørensen_coefficient

    Other variations include the "similarity coefficient" or "index", such as Dice similarity coefficient (DSC). Common alternate spellings for Sørensen are Sorenson , Soerenson and Sörenson , and all three can also be seen with the –sen ending (the Danish letter ø is phonetically equivalent to the German/Swedish ö, which can be written as oe ...

  5. Global distance test - Wikipedia

    en.wikipedia.org/wiki/Global_distance_test

    The global distance test (GDT), also written as GDT_TS to represent "total score", is a measure of similarity between two protein structures with known amino acid correspondences (e.g. identical amino acid sequences) but different tertiary structures.

  6. Category:Similarity measures - Wikipedia

    en.wikipedia.org/wiki/Category:Similarity_measures

    Pages in category "Similarity measures" The following 10 pages are in this category, out of 10 total. This list may not reflect recent changes. ...

  7. Similarity learning - Wikipedia

    en.wikipedia.org/wiki/Similarity_learning

    Similarity learning is closely related to distance metric learning.Metric learning is the task of learning a distance function over objects. A metric or distance function has to obey four axioms: non-negativity, identity of indiscernibles, symmetry and subadditivity (or the triangle inequality).

  8. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the ...

  9. Similarity - Wikipedia

    en.wikipedia.org/wiki/Similarity

    Similarity (geometry), the property of sharing the same shape; Matrix similarity, a relation between matrices; Similarity measure, a function that quantifies the similarity of two objects Cosine similarity, which uses the angle between vectors; String metric, also called string similarity; Semantic similarity, in computational linguistics