Novel Two-Dimensional Visualization Approaches for Multivariate Centroids of Clustering Algorithms

Algorithm 2

K-means++ and mapping by QGA.

	Input: Number of the centroids, k, iteration number (it)
	Output: Map with C placed, M
	Begin
	QM: Population containing individual qubit matrices
	PM: Population containing individual probability matrices
	qm: Length of QM
	pm: Length of PM
	tr ≔ 0
	C: Set of the centroids returned by Algorithm 1
	Repeat
	For a = 1 : qm
	For i = 1 : c
	For j = 1 : r
	QM ·   ≔
	QM ·  ≔
	For a = 1 : pm
	makeOperator(it, PMa)
	QM = updateOperator(QM)
	tr ≔ tr + 1
	Until tr = it
	Return M having the fittest value
	End