AbstractA cluster is a family of variables showing high internal correlation. For example, conductivity, hardness and Ca‐concentration which generally appear with correlation coefficients (r) > 0.8 for mean annual values among lakes. They indicate the amount of salts and ions in the water. A functional group is a family of variables which describe a particular process or mechanism in an ecosystem, like mean depth relative depth and volume development. All of these could be related to resuspension. Such variables would also constitute a cluster if they are well correlated across many lakes belonging to a given lake type. These three parameters express different form elements of lakes, they belong to the same functional group in contexts of lake resuspension, but they do not constitute a cluster since they are only rather poorly correlated (the r‐values between these parameters is generally <0.5). A variant is a value for a given variable from a defined period of time, like a mean annual or monthly value. One can then ask: Which variant from which time period should be used in relation to a given y‐variable one wants to predict to obtain a model with a high predictive power? Variables belonging to the same cluster can often replace one another in models without significantly altering predictive accuracy. The aim of this work is to determine clusters among standard groups of water variables, lake morphometric parameters and catchment parameters. The analysis uses a comprehensive data‐set from 95 Swedish lakes. There are about 83,000 lakes in Sweden, about 81,000 belong to this lake type of glacial lakes, which is the most common lake type on Earth. Selected results: Among the catchment parameters, one may note that the proportion of lakes does not co‐vary closely with any other parameter, but that the percentage of morainic soils is negatively associated with the area covered by bedrock and flat rocks. Two clusters of morphometric parameters can be identified: Size parameters (e.g., volume and area) and form parameters (e.g., relative depth and dynamic ratio). Among the water variables, colour, iron concentration and Secchi depth are strongly correlated. The concentration of total phosphorus, which is functionally associated with the production of algae, is also related to Secchi depth.