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 The Euclidean distance algorithm uses the following procedure to determine the line of discrimination between the data sets: Compute the mean of each data set, which is nothing more that a simple average of the x and y coordinates. Determine the points in the current space that are equal in distance from the means of the data sets. The points that are equal in distance from the means of each data set determine the line of discrimination that separates them. The distance between any two points in the current space is determined by the following distance formula: d2 = (x2 - x1)2 + (y2 - y1)2 Here is an example of how the Euclidean distance scheme works: First select the Two Gaussian data set from the Patterns menu. Next, select Euclidean Distance under the Algorithms menu. Initialize this algorithm by selecting Initialize from the Go menu. In order to compute the line of discrimination, select the Next option under the Go menu. This will display the first step of the process, i.e., it will display the data sets in both the input plot (top left) and the output plot (bottom left). The process description box further indicates the step that we are currently on and the algorithm that is being used to compute the line of discrimination. The second step of the process computes the mean of each data set. The means of the data sets are then displayed on the output plot as black dots. The values of the means of each data set, which correspond to the current scale, are then displayed on the process description box. The third step of the process displays the line of discrimination, given the current data sets, as computed by the Euclidean distance algorithm. The classification errors for each data set, along with the total classification error, are then displayed on the process description box. Click here to go back to the main tutorial page.