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Scree Plot
In multivariate statistics, a scree plot is a line plot of the eigenvalues of factors or principal components in an analysis. The scree plot is used to determine the number of factors to retain in an exploratory factor analysis (FA) or principal components to keep in a principal component analysis (PCA). The procedure of finding statistically significant factors or components using a scree plot is also known as a scree test. Raymond B. Cattell introduced the scree plot in 1966. A scree plot always displays the eigenvalues in a downward curve, ordering the eigenvalues from largest to smallest. According to the scree test, the "elbow" of the graph where the eigenvalues seem to level off is found and factors or components to the left of this point should be retained as significant. Etymology The scree plot is named after the elbow's resemblance to a scree in nature. Criticism This test is sometimes criticized for its subjectivity. Scree plots can have multiple "elbows" that make ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Scree
Scree is a collection of broken rock fragments at the base of a cliff or other steep rocky mass that has accumulated through periodic rockfall. Landforms associated with these materials are often called talus deposits. The term ''scree'' is applied both to an unstable steep mountain slope composed of rock fragments and other debris, and to the mixture of rock fragments and debris itself. It is loosely synonymous with talus, material that accumulates at the base of a projecting mass of rock, or talus slope, a landform composed of talus. The term ''scree'' is sometimes used more broadly for any sheet of loose rock fragments mantling a slope, while ''talus'' is used more narrowly for material that accumulates at the base of a cliff or other rocky slope from which it has obviously eroded. Scree is formed by rockfall, which distinguishes it from colluvium. Colluvium is rock fragments or soil deposited by rainwash, sheetwash, or slow downhill creep, usually at the base of gentle ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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. The same method can be used to choose the number of parameters in other data-driven models, such as the number of principal components to describe a data set. The method can be traced to speculation by Robert L. Thorndike in 1953. Intuition Using the "elbow" or "knee of a curve" as a cutoff point is a common heuristic in mathematical optimization to choose a point where diminishing returns are no longer worth the additional cost. In clustering, this means one should choose a number of clusters so that adding another cluster doesn't give much better modeling of the data. The intuition is that increasing the number of clusters will naturally improve the fit (explain more of the variation), since there are mo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
