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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.
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.
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:
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.
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).
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
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:
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