The LBG-Clustering algorithm uses the following procedure to determine the line of discrimination between the data sets: - First, set the number of iterations (default = 10) from the
**Edit->Settings**menu. For this exercise, we will use the default value. - Initially all data that is entered is pooled together into one large
cluster. After creating the initial cluster, a centroid is generated
be computing the mean of the initial cluster. Once the cluster and
centroid have been generated classification begins. Classification
involves iterating over all points in the cluster and creating new
clusters based on their proximity to the centroid/centroids.
- After the new clusters are generated the old centroid/centroids is/are
replaced with new ones. The new centroids are generated by first
computing the means of the new clusters. The new centroids are then
determined by taking points which are one standard deviation towards
the left and one standard deviation towards the right of the means.
- The whole process above is repeated N times (N is the number of
iterations) with the intent that eventually the line of
discrimination between the data sets will converge.
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