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  2. Cut (graph theory) - Wikipedia

    en.wikipedia.org/wiki/Cut_(graph_theory)

    If s and t are specified vertices of the graph G, then an s – t cut is a cut in which s belongs to the set S and t belongs to the set T. In an unweighted undirected graph, the size or weight of a cut is the number of edges crossing the cut. In a weighted graph, the value or weight is defined by the sum of the weights of the edges crossing the ...

  3. Leverage (statistics) - Wikipedia

    en.wikipedia.org/wiki/Leverage_(statistics)

    The formula then divides by () to account for the fact that we remove the observation rather than adjusting its value, reflecting the fact that removal changes the distribution of covariates more when applied to high-leverage observations (i.e. with outlier covariate values). Similar formulas arise when applying general formulas for statistical ...

  4. Curve fitting - Wikipedia

    en.wikipedia.org/wiki/Curve_fitting

    Extrapolation refers to the use of a fitted curve beyond the range of the observed data, [16] and is subject to a degree of uncertainty [17] since it may reflect the method used to construct the curve as much as it reflects the observed data. For linear-algebraic analysis of data, "fitting" usually means trying to find the curve that minimizes ...

  5. Calculus on finite weighted graphs - Wikipedia

    en.wikipedia.org/wiki/Calculus_on_finite...

    Differential equations or difference equations on such graphs can be employed to leverage the graph's structure for tasks such as image segmentation (where the vertices represent pixels and the weighted edges encode pixel similarity based on comparisons of Moore neighborhoods or larger windows), data clustering, data classification, or ...

  6. Statistical graphics - Wikipedia

    en.wikipedia.org/wiki/Statistical_graphics

    In addition, the choice of appropriate statistical graphics can provide a convincing means of communicating the underlying message that is present in the data to others. [1] Graphical statistical methods have four objectives: [2] The exploration of the content of a data set; The use to find structure in data; Checking assumptions in statistical ...

  7. Kőnig's theorem (graph theory) - Wikipedia

    en.wikipedia.org/wiki/Kőnig's_theorem_(graph...

    If G is a graph, the line graph L(G) has a vertex for each edge of G, and an edge for each pair of adjacent edges in G. Thus, the chromatic number of L(G) equals the chromatic index of G. If G is bipartite, the cliques in L(G) are exactly the sets of edges in G sharing a common endpoint. Now Kőnig's line coloring theorem, stating that the ...

  8. Finite difference method - Wikipedia

    en.wikipedia.org/wiki/Finite_difference_method

    For example, consider the ordinary differential equation ′ = + The Euler method for solving this equation uses the finite difference quotient (+) ′ to approximate the differential equation by first substituting it for u'(x) then applying a little algebra (multiplying both sides by h, and then adding u(x) to both sides) to get (+) + (() +).

  9. Elbow method (clustering) - Wikipedia

    en.wikipedia.org/wiki/Elbow_method_(clustering)

    In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters and picking the elbow of the curve as the number of clusters to use.