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  2. Jaccard index - Wikipedia

    en.wikipedia.org/wiki/Jaccard_index

    In this scenario, the similarity between the two baskets as measured by the Jaccard index would be 1/3, but the similarity becomes 0.998 using the SMC. In other contexts, where 0 and 1 carry equivalent information (symmetry), the SMC is a better measure of similarity.

  3. Similarity measure - Wikipedia

    en.wikipedia.org/wiki/Similarity_measure

    Similarities among 162 Relevant Nuclear Profile are tested using the Jaccard Similarity measure (see figure with heatmap). The Jaccard similarity of the nuclear profile ranges from 0 to 1, with 0 indicating no similarity between the two sets and 1 indicating perfect similarity with the aim of clustering the most similar nuclear profile.

  4. MinHash - Wikipedia

    en.wikipedia.org/wiki/MinHash

    The Jaccard similarity coefficient is a commonly used indicator of the similarity between two sets. Let U be a set and A and B be subsets of U, then the Jaccard index is defined to be the ratio of the number of elements of their intersection and the number of elements of their union:

  5. Simple matching coefficient - Wikipedia

    en.wikipedia.org/wiki/Simple_matching_coefficient

    In this scenario, the similarity between the two baskets as measured by the Jaccard index would be 1/3, but the similarity becomes 0.998 using the SMC. In other contexts, where 0 and 1 carry equivalent information (symmetry), the SMC is a better measure of similarity.

  6. Chemical similarity - Wikipedia

    en.wikipedia.org/wiki/Chemical_similarity

    The most popular similarity measure for comparing chemical structures represented by means of fingerprints is the Tanimoto (or Jaccard) coefficient T. Two structures are usually considered similar if T > 0.85 (for Daylight fingerprints).

  7. Beta diversity - Wikipedia

    en.wikipedia.org/wiki/Beta_diversity

    When there are two subunits, and presence-absence data are used, this measure as ranged to the interval [0, 1] equals the one-complement of the Jaccard similarity index. [ 2 ] β-diversity patterns

  8. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Jaccard index; The Jaccard index is used to quantify the similarity between two datasets. The Jaccard index takes on a value between 0 and 1. An index of 1 means that the two dataset are identical, and an index of 0 indicates that the datasets have no common elements. The Jaccard index is defined by the following formula:

  9. Jaccard similarity - Wikipedia

    en.wikipedia.org/?title=Jaccard_similarity&...

    This page was last edited on 28 September 2011, at 03:31 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.