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This concept can be extended for functionals instead of means as well.
In this paper, we will give some results in the case where instead of means we have power sums.
K-medians clustering is a variation of K-means, however mathematically medians are calculated instead of means,[13].
Thus, for our second analysis, we used medians instead of means, on the grounds that they are less sensitive to outliers.
We follow Majumder et al. (2012) and estimate equation (4) by using medians instead of means which is a more robust statistic with respect to outliers.
These methods are looking for medoids, instead of means, which are the "most central elements" of each cluster and are selected from the points in the dataset.
Similar(38)
This probabilistic engineering design analysis assumes probability distributions of design parameters, instead of mean values only.
δMedian is the standard deviation calculated from median instead of mean.
As VM's resource utilization can be very dynamic in real world, instead of mean we can use median and we can modify the formula for standard deviation using median instead of mean.
In our implementation, we extend the approach to do the multi-class classification and use K-means clustering instead of mean-shift clustering to improve the computational complexity.
In the rocky core, we solve Eq. (4) by using parameters for rocks instead of mean values of the ice and rock mixture.
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