## Data Transformations

Choice depends on data set!

• Center and standardize
1. Center: subtract from each value the mean of the corresponding vector
2. Standardize: devide by standard deviation
• Result: Mean = 0 and STDEV = 1
• Center and scale with the scale() function
1. Center: subtract from each value the mean of the corresponding vector
2. Scale: divide centered vector by their root mean square (rms): $x_{rms} = \sqrt[]{\frac{1}{n-1}\sum_{i=1}^{n}{x_{i}{^2}}}$
• Result: Mean = 0 and STDEV = 1
• Log transformation
• Rank transformation: replace measured values by ranks
• No transformation

## Distance Methods

List of most common ones!

• Euclidean distance for two profiles X and Y: $d(X,Y) = \sqrt[]{ \sum_{i=1}^{n}{(x_{i}-y_{i})^2} }$
• Disadvantages: not scale invariant, not for negative correlations
• Maximum, Manhattan, Canberra, binary, Minowski, …
• Correlation-based distance: 1-r
• Pearson correlation coefficient (PCC): $r = \frac{n\sum_{i=1}^{n}{x_{i}y_{i}} - \sum_{i=1}^{n}{x_{i}} \sum_{i=1}^{n}{y_{i}}}{ \sqrt[]{(\sum_{i=1}^{n}{x_{i}^2} - (\sum_{i=1}^{n}{x_{i})^2}) (\sum_{i=1}^{n}{y_{i}^2} - (\sum_{i=1}^{n}{y_{i})^2})} }$
• Spearman correlation coefficient (SCC)
• Same calculation as PCC but with ranked values!

There are many more distance measures

• If the distances among items are quantifiable, then clustering is possible.
• Choose the most accurate and meaningful distance measure for a given field of application.
• If uncertain then choose several distance measures and compare the results.