The n0× n0 adjacency matrix C0 gives the edge weights between each data point pair and the n0× n0 diagonal degree matrix D0 (Step 1) gives the connectivity at each node in that D t (i, i) contains the total number of connections to node i at hierarchy level t.
Yonezawa and Takeuchi (2001) used coherence obtained between a data pair including the 1995 Hyogoken-nanbu earthquake and a pair before the earthquake to show that the decrease in interferometric coherence was a good representative of the damaged urban area.
Because data points of different classes might overlap, we added a weight to the interval such as (2% ∼ 10%), depending on the distance between the data pair of different types.
Instead of using cluster labels, the proposed method uses the similarity between data pair, which is the weighted combination between distance and target-class agreement.
The data analysis yielded an R-value, which is a correlation representing the linear relationship between the data pairs.
These estimates are typically provided by estimating the covariance directly or, more commonly, the semivariance, γ, between data pairs at a series of regular lags, taking the average at each lag, and fitting a continuous model to these averages.
Asterisks indicate significant (P < 0.01) differences between the corresponding data pairs.
Including all measurements, correlations between data pairs were established using linear regression analysis and are expressed as the square of Pearson's correlation coefficients (r).
Shapiro Wilk normality tests were used to determine whether diameter measurements were normally distributed, and F-tests to compare variances between data pairs.
Including all consecutive hemodynamic measurements per patient, correlations between data pairs were established using linear regression analysis and are expressed as the square of Pearson's correlation coefficients (r).
