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Then, for the second step, the distances between each data sample and the centroids are calculated.
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After the cluster center for each class is identified, the distance between a data sample and its cluster center (or intra-cluster center) can be calculated.
The first distance-based feature means the distance between the data sample and its cluster center.
The second one is the sum of the distances between the data sample and other cluster centers.
In this article, we will utilize the distance between a data sample and its intra-cluster center as a new feature, called Feature 1.
Here, we define another new feature, called Feature 2, as the sum of the distances between a data sample and its extra-cluster centers.
As a result, the distance from D7 to its intra-cluster center (C3) is determined by the Euclidean distance from D7 to C3. Figure 1 The distance between the data sample and its intra-cluster center.
One estimate of σ2 is based on the variability between the sample means and the variability between each sample data".
Signal restoration was performed on each individual part by finding the best compromise between the data samples and the smoothing criteria.
The restoration method between regular data samples utilizes all sampled points and the smoothness of each subset to estimate the best fit.
We outline a K-way spectral clustering algorithm able to integrate pairwise relationships between the data samples.
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