[Retracted] A Dynamic Density Peak Clustering Algorithm Based on K-Nearest Neighbor

Algorithm 2

DDPC Algorithm.

	Input: Dataset  = ,
	Output: Clusters = {, }
	//Calculate the local density of each data.
	for each data point in do
	for each data point in do
	Calculate the distance between and
	end for
	Sort the first data according to the distance from small to large:  =
	Calculate the average distance from each neighbor
	end for
	The average distance matrix of neighbors of each node (scanning distance) is obtained:  =
	//The adaptive adjustment range is determined according to parameters K//and R.
	for each in do
	Calculate the number of neighbors whose average distance is smaller than the node:
	ifthen
	is a high-density point:
	end if
	end for
	Int  = 1
	//Adaptive clustering
	for each in do
	If has no cluster label then
	end if
	for each in do
	for each in do
	if the distance between and is less than or then
	If has cluster label then
	Change all ’s cluster labels to
	else
	end if
	break
	end if
	end for
	end for
	++
	end for
	For the points without cluster label, KNN algorithm is used for clustering