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

Algorithm 4

The weighted K-means++ and mapping by Pearson's correlation.

	Input: Number of the centroids, k
	Output: Map with C placed, M
	Begin
	C′: Set of the centroids obtained by the traditional K-means++ clustering in Algorithm 1
	Ω: Set of the clusters of the instances in I, computed by C′
	I′: Set of the instances, with a new attribute as the target by filling it with Ω,
	CC: Set of the weight values obtained by Pearson’s correlation feature selection method
	C: Set of the centroids obtained by the traditional K-means++ clustering in Algorithm 3
	fc: The highest ranked feature in CC
	FC: Set of the values in the fcth feature in C
	wc: Sum of the scores in the features except the fcth feature
	WC: Set of the average of the values in the other features
	For i = 1 : k
	FCi = Ci,fc
	For i = 1 : k
	For j = 1 : f
	If j is not equal to fc
	WCi = WCi + Ci,j
	For i = 1 : f
	If j is not equal to fc
	wc = wc + CCi
	For i = 1 : k
	WCi = WCi/wc
	minfc: The minimum value is in FC
	maxfc: The maximum value is in FC
	minwc: The minimum value is in WC
	maxwc: The maximum value is in WC
	For i = 1 : k
	For j = 1 : f
	Ei,j = [Ci,j − minj]c/(maxj − minj)
	Return M where the centroids in E are mapped
	End