Algorithm 1. ID-SVDD outlier detection
Input:	Target dataset X = {x1, x2, …xi, i = 1, 2, …, n}, kernel function K(.)
Output:	αi, R, and r.
Begin
Define an array P to store relative density weight for each point.
for (k = 1; k ≤ n; k++) do
calculate Pk = ρ(xk) according to Equation (12)
End
Solve the optimization problem of (8).
Determine a sample whose αi is between 0 and ρ(xi)C.
Calculate the radius R of sphere and the distance r according to Equations (3) and (4).
End
Returnαi, R and r.