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 The nearest neighbor algorithm is similar to the Euclidean distance algorithm in that the line of discrimination is computed using the following steps: Determine the points in the current space that are equal in distance from the nearest point of each data set. The points that are equal in distance from the nearest point of each data set determine the line of discrimination that separates the 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 nearest neigbour scheme works: First select the Two Gaussian data sets from the Patterns menu. Following that, select the Nearest Neigbor option 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, the data sets in both the input plot (top left) and the output plot (bottom left) of the applet. Also, the process description box indicates which step you are currently on and the algorithm that is currently being used to compute the line of discrimination. Note that the nearest neighbor algorithm is computationally expensive so please be patient if the process takes longer. The second step of the process computes the mean of the each data set. The mean of each data sets is displayed on the output plot as black dots near the corresponding data sets. The value of the mean for each data set, which corresponds to the current scale, is displayed on the process description box. The third step of the process displays the line of discrimination of the given data sets as determined by the nearest neighbor algorithm. Also, the classification error for each data set along with the total classification error is displayed on the process description box. Click here to go back to the main tutorial page.