COMMunication
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• Euclidian distance Distance
• Manhattan distance (City-block) Distance
• Correlation Distance
• Eisen Cosine Correlation Distance
• Kendal Distance

$$\begin{eqnarray*} d_{euc} (x, y) & = & \sqrt{ \sum_{i=1}^{n}(x_{i} - y_{i})^2 } \\ d_{man} (x, y) & = & \sum_{i=1}^{n} | (x_{i} - y_{i}) | \\ d_{cor} (x, y) & = & 1 - \frac { \displaystyle \sum_{i=1}^{n}(x_{i} - \overline{x}) (y_{i} - \overline{y})} { \sqrt{ \displaystyle \sum_{i=1}^{n}(x_{i} - \overline{x})^2 \displaystyle \sum_{i=1}^{n}(y_{i} - \overline{y})^2 }} \\ d_{eisen} (x, y) & = & 1 - \frac {\left| \displaystyle \sum_{i=1}^{n} x_{i} \; y_{i} \right| } { \sqrt{ \displaystyle \sum_{i=1}^{n}x_{i}^{2} \displaystyle \sum_{i=1}^{n} y_{i}^2 }} \\ d_{kend} (x, y) & = & 1- \displaystyle \frac { n_{c} - n_{d} } { \displaystyle \frac{1}{2} n(n-1)} \\ \end{eqnarray*}$$