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Kaufman's initialization can provide a set of initial centers of clusters to produce stable and accurate clustering results for remote sensing images.
The accuracy of the K-means clustering algorithm depends upon the number of clusters that are specified at the initialization; depending upon the choice of the initial centers, the clustering results could vary significantly.
Since the result of K-means depends on the initial centers, we repeat it with 10 random initializations from which the one resulting in the smallest objective function value is selected.
In the proposed approach, initial centers are randomly selected from the calibration set.
In order to overcome the sensitivity of the method to the choice of the initial cluster centers, the initial centers are selected using features extracted from simulated signals, resulting from a well established model for the dynamic behavior of defective rolling element bearings.
Experimental results show that k-means clustering based on ranked set sampling is more efficient than other clustering techniques such as the k-means based on the fundamental sampling design simple random sampling technique, the standard k-means and the k-means based on the Bradley refinement of initial centers.
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To deal with the problem of the initial center selection and outliers, a set of statistic methods is conducted.
Hurd YL, Yoon M, Manini AF, Hernandez S, Olmedo R, Ostman M, et al. Early phase in the development of cannabidiol as a treatment for addiction: opioid relapse takes initial center stage.
Our system takes approximately 3.2 seconds to perform a full horizontal sweep and 0.7 seconds to scan vertically to either its maximum upward position or its minimum downward position from its initial center position.
Step 5: Select the initial center of unchanged and changed classes based on certainty regions.
Similarly, the means of MDI and SAI based on Eq. (12) are used to be the initial center of changed class.
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