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Table 4 Restrictions and limitations for clusterings

From: A practical approach to cluster validation in the energy sector

Name Explanation
Direct Rating Every decision variable is assigned with an importance independent of the others (as in Likert scale questionnaires).
Ranking Method Decision variables are ranked relative to one other. These ranks can be used to calculate weights using rank sum, rank reciprocal or rank exponent method.
Point Allocation Decision makers allocate weights directly to decision variables. The result is normalized.
Pairwise Comparison Method Decision variables are compared pairwise and the resulting pairwise weights are documented in a matrix. The resulting matrix is used to calculate the overall weights and a consistency ratio.
Swing Weighting Method All decision variables are set to the worst score. Decision makers can change the score of individual variables by moving them to the best score. The rank of doing so determines the importance (Leijten et al. 2017).
Graphical Weighting Method This graphical method utilizes a horizontal line to place decision variables relative to one other. Their distance determines their assigned weights.
(Revised) SIMOS Weighting Method Decision variables are ranked relative to one other. Variables may share the same rank. The relative ranks can be increased by inserting empty ranks in between. In the last step, decision makers need to decide how many times more important the first variable is compared to the last. This rank is used to assign weights.
Fixed Point Scoring Decision makers need to distribute a finite number of points to weigh decision variables.