CFAR-m Differentiators

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What makes CFAR-m different from the competition

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CFAR-m Differentiators

  1. 1. CFAR-m Differentiators CFAR-m CompetitorsObjectivity There is no manipulation of the Unique to CFAR-m weightings: the weighting extracted is completely objective since it comes from the information contained within the variables themselves and from This characteristic represents a clear their internal dynamic. competitive advantage compared to other aggregation/ranking methods.Ranking vs Clustering Automatically provides a ranking only Unique to CFAR-m according to the actual data provided (proprietary method) and not only a simple clustering.Specificity A specific equation calculates each Unique to CFAR-m individual item’s score (composite indicator)Decision-support Allows for the running of simulations Unique to CFAR-m and then proposes to decision makers various plans of action and an optimal sequence of reforms.Contribution of variables For each individual variable CFAR-mto ranking indicates its precise contribution to the rankingIdentifies Non-linearity Because CFAR-m works with Neural This is very useful in the area of data Networks it can identify non-linear analysis. relationships between variables

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