Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (i.e., uncorrelated with) the preceding components.
TOPSIS METHOD T echnique of O rder P reference by S imilarity to I deal S olution This method considers three types of attributes or criteria Qualitative benefit attributes/criteria Quantitative benefit attributes Cost attributes or criteria
TOPSIS METHOD In this method two artificial alternatives are hypothesized : Ideal alternative : the one which has the best level for all attributes considered. Negative ideal alternative : the one which has the worst attribute values. TOPSIS selects the alternative that is the closest to the ideal solution and farthest from negative ideal alternative.
Input to TOPSIS TOPSIS assumes that we have m alternatives (options) and n attributes/criteria and we have the score of each option with respect to each criterion. Let x ij score of option i with respect to criterion j We have a matrix X = (x ij ) m n matrix. Let J be the set of benefit attributes or criteria (more is better) Let J ' be the set of negative attributes or criteria (less is better)
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