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  2. Venn diagram - Wikipedia

    en.wikipedia.org/wiki/Venn_diagram

    A Venn diagram is a widely used diagram style that shows the logical relation between sets, popularized by John Venn (1834–1923) in the 1880s. The diagrams are used to teach elementary set theory, and to illustrate simple set relationships in probability, logic, statistics, linguistics and computer science.

  3. Data and information visualization - Wikipedia

    en.wikipedia.org/wiki/Data_and_information...

    These diagrams depict elements as points in the plane, and sets as regions inside closed curves. A Venn diagram consists of multiple overlapping closed curves, usually circles, each representing a set. The points inside a curve labelled S represent elements of the set S, while points outside the boundary represent elements not in the set S.

  4. John Venn - Wikipedia

    en.wikipedia.org/wiki/John_Venn

    John Venn, FRS, [2] [3] FSA [4] (4 August 1834 – 4 April 1923) was an English mathematician, logician and philosopher noted for introducing Venn diagrams, which are used in logic, set theory, probability, statistics, and computer science.

  5. Set theory - Wikipedia

    en.wikipedia.org/wiki/Set_theory

    Set theory is the branch of mathematical logic that studies sets, which can be informally described as collections of objects.Although objects of any kind can be collected into a set, set theory – as a branch of mathematics – is mostly concerned with those that are relevant to mathematics as a whole.

  6. Information diagram - Wikipedia

    en.wikipedia.org/wiki/Information_diagram

    Information diagrams have also been applied to specific problems such as for displaying the information theoretic similarity between sets of ontological terms. [ 3 ] Venn diagram showing additive and subtractive relationships among various information measures associated with correlated variables X and Y .

  7. Methodology - data.huffingtonpost.com

    data.huffingtonpost.com/2016/we-the-tweeple/...

    The sentence below each Venn diagram describes this over-indexing: The followers who over-index are (index - 1) × 100% more likely to use a term than the followers who under-index. We have published all our source code: tweep-followers downloads large numbers of Twitter followers’ profile information.

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