Cluster analysis algorithms

Ward's method

In statistics, Ward's method is a criterion applied in hierarchical cluster analysis. Ward's minimum variance method is a special case of the objective function approach originally presented by Joe H. Ward, Jr. Ward suggested a general agglomerative hierarchical clustering procedure, where the criterion for choosing the pair of clusters to merge at each step is based on the optimal value of an objective function. This objective function could be "any function that reflects the investigator's purpose." Many of the standard clustering procedures are contained in this very general class. To illustrate the procedure, Ward used the example where the objective function is the error sum of squares, and this example is known as Ward's method or more precisely Ward's minimum variance method. The nearest-neighbor chain algorithm can be used to find the same clustering defined by Ward's method, in time proportional to the size of the input distance matrix and space linear in the number of points being clustered. (Wikipedia).

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From playlist Solve a System of Equations Using Elimination | Hard

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From playlist Eigenvalues

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From playlist Solve a System of Equations Using Elimination | Hard

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From playlist Solve a System of Equations Using Elimination | Medium

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From playlist Solve a System of Equations Using Elimination | Medium

Related pages

Nearest-neighbor chain algorithm | Complete-linkage clustering | Single-linkage clustering | Euclidean distance | Distance matrix | Statistics | Hierarchical clustering | Lack-of-fit sum of squares