Density Peaks Clustering Based on Feature Reduction and Quasi-Monte Carlo

Algorithm 1

QMC-DPC.

Input:
The Data set:
Output:
Clustering results
Steps:
(1)	Perform feature reduction on to obtain low-dimensional feature data ;
(2)	Generate Quasi-Monte Carlo points and determine circular data unit on ;
(3)	Count the density for each circular data unit and generate and ;
(4)	Calculate the matrix based on and remove the zero elements in . Sort the remaining elements and determine the intercept ;
(5)	Calculate the and for each nonempty Quasi-Monte Carlo points by equations (3) and (4);
(6)	Draw the decision graph to select cluster centers and determine the number of ;
(7)	According to the principle of nearest distance, assign the remaining nonempty Quasi-Monte Carlo Points;
(8)	Assign the data points to the class of the nearest nonempty Quasi-Monte Carlo Points;
(9)	Return the clustering results .