How do I choose the clustering parameters ?
    Upon loading your data, and after choosing the clustering model, you must specifiy some run parameters as shown in the next figure.


Edit parameters Dialog

    The "Ok" button means that you will run the algorithm with the parameter values given in the "New value" column.

    The "Reset" button will set all the values in the "New value" column to those in the "Current value" column, invalidating all changes you made. The button is enabled only when you change at least one value.

    The "Cancel" button will return to the previous screen.

    Select a parameter line and press Return, or double-click the line, to change the value of the parameter. If a parameter is modified, the new value appears in the "New value" column, and the field is marked by a red background (Macintosh) or a red arrow (PC). A short explanatory text on the currently selected option is shown in the space immediately above (Macintosh) or below (PC, shown in the above figure) the list of parameters.


The parameters have the following meaning.

a) Log-transform data values

This parameter lets you choose whether all activity values in all profiles should be replaced by their logarithm before any further processing. It is recommended to select this option when the measurements represent ratios of activities.

b) Distance Measure

Whatever clustering criterion is selected, this parameter lets you choose the measure that is used to compute the distance between two profiles. The following possibilities are available:

- Pearson Coefficient: this is the popular Pearson coefficient (also known as Pearson correlation), with Arc-Cosine transformation (learn more). This distance implies both Zero-mean normalization and Unit-norm normalization (learn more).
- Correlation (standard): the standard correlation with Arc-Cosine transformation (learn more). This distance implies Unit-norm normalization (learn more).
- Euclidean: the standard Euclidean distance, with no normalization


NOTE

    Note that with both the Pearson and the standard correlation, the Arc-Cosine transformation is applied, yielding a well-defined metric for both distance measures (learn more)