Before starting clustering several important steps have to be performed:

• Check if the used data is metric: In clustering, the primary measure is Euclidian distance (in most cases), which requires numeric data. While it is possible to encode some arbitrary data using numerical values, they must maintain the semantics of numbers, i.e. 1 < 2 < 3. Good examples of natural metric data are temperature, exam assessments or alike. Bad examples: gender, colour.
• Select the proper scale: For the same reasons as the distance measure, the values of each dimension should be on the same scale. For instance, customers' monthly incomes in euros and their credit ratios are typically at different scales – the incomes in thousands, while ratios between 0 and 1. If scales are not adjusted, the income dimension will dominate distance estimation among points, deforming the overall clustering results. A universal scale is usually applied to all dimensions to avoid this trap. For instance:
  • Unity interval: a minimal factor value is substructed from the given point value and divided by the interval value, giving the result 0 to 1.
  • Z-scale: The factor's average value is substructed from the original value of the given point and then divided by the factor's standard deviation, which provides results distributed around 0 with a standard deviation of 1.