Following initialization, Euclidean distances between each data point and the centroids are calculated and each data point is assigned to a cluster based on its nearest centroid.
We examined repeatability by measuring the same phantom five times over the course of one week, turning the system off in between each data set, and recording the data with both a constant gain setting and variable gain settings.
The difference in pixel intensity between each data point and every other data point in a given direction was calculated and these differences were binned according to separation distance (h) (see horizontal axes in Figure 3).
It defines the inter-cluster separation as the minimum square distance between cluster centers, and the intra-cluster compactness as the mean square distance between each data object and its cluster center.
Thus, a new statistical procedure was introduced here to consider both the retrieval score and the lesion extent by plotting the data as in Fig. 2C and D but computing a line defined by minimizing the sum of distances between each data point and its projection onto the line (dashed lines in Fig. 2E and F).
Then, for the second step, the distances between each data sample and the centroids are calculated.
Compute the distance between each data point and cluster center through (mathcal{{M}} ( phi,p,mathcal{{F}} )) distance measure.
The difference between each data point and the dashed line show the degree of correlation between the Pi2 disturbance and the auroral arc.
Step-2: Compute the distance between each data point and cluster center through (mathcal{{M}} ( phi,p,mathcal{{F}} )) distance measure.
By using (M phi)) or (N phi)) distance measure (since both are dual of each other, in application point of view, we only consider (M phi))), compute the distance between each data points and cluster centers.
