Adaptive Mixed-Attribute Data Clustering Method Based on Density Peaks

Algorithm 1

Find cluster center.

Input: rho, delta (represent local density vector ρ and relative distance vector δ)
Output: Sc (set of cluster centers Sc)
(1)	//Step 1. Calculate .
(2)	for i = 1 to length(rho) do
(3)	gamma(i) = rho(i)  delta(i)
(4)	end
(5)	//Step 2. Sort rho, delta, gamma (ρ, δ, γ) in descending order:
(6)	Sorted_rho = sort (rho, “descend”);
(7)	Sorted_delta = sort(delta, “descend”);
(8)	Sorted_gamma = sort(gamma, “descend”);
(9)	//Step 3. Calculate the inflection point of rho, delta, gamma using equation (7) separately and construct the three candidate sets , Sp, and Sd.
(10)	Sp = calcinflection(Sorted_rho);
(11)	Sd = calcinflection(Sorted_delta);
(12)	= calcinflection(Sorted_gamma);
(13)	//Step 4. Calculate the intersections of the three sets and return the result Sc.
(14)	Sc = intersection(Sp,Sd,).