Characterizing the clusters by their mean values

Another way to look at the clusters is by looking directly at their mean values. We can do this directly by using SQL:

• First, look at any variables which have normalized values >1 or < -1, or high the highest absolute value for that variable. That will give you some clues on how to begin to classify the clusters.
• Also look at the magnitude and the signs of the coefficients. Coefficients with large absolute values can indicate an important influence of the variable on that particular cluster. Variables with opposite signs are important in terms of characterizing or naming the clusters.

        tmp_agg <- SparkR::sql("SELECT prediction, mean(age),
        mean(triceps),      
        mean(pregnant),mean(pressure),mean(insulin),
        mean(glucose),
        mean(pedigree) from fitted_tbl group by 1") 
        head...