[data clustering] realize data clustering analysis based on multiverse optimization DBSCAN with matlab code

1 brief introduction

in the light of DBSCAN Clustering algorithm is sensitive to parameters , Parameter selection depends on experience , In this paper, a multiverse optimization based DBSCAN clustering (MVO-DBSCAN) Algorithm .

2 Part of the code

%%% main function： The main function clc;clear;close all;tic;%  Reading data % load('C:\Users\Administrator\Desktop\MATLAb Programming practice\MVO-DBSCAN\X.mat');load X.mat;%  Data labels train_labels=[];for i=1:3    train_labels=[train_labels;i*ones(100,1)];end%% run MVO Algorithm​Universes_no=60; %Number of search agents (universes)Max_iteration=500; %Maximum numbef of iterations​%  Parameters to be optimized ( The universe ) Above 、 Lower bounds and dimensions lb=0.01;ub=0.5;dim=1;​%  Defining parameters MinPtsMinPts =4;[Best_score,Best_pos,cg_curve]=MVO(Universes_no,Max_iteration,lb,ub,dim,MinPts,X,train_labels);​display(['The best solution obtained by MVO is : ', num2str(Best_pos)]);display(['The best optimal value of the objective funciton found by MVO is : ', num2str(Best_score)]);​%% Run DBSCAN Clustering AlgorithmEps=Best_pos;labels=DBSCAN(X,Eps,MinPts);figure;PlotClusterinResult(X, labels);title(['DBSCAN Clustering (\epsilon = ' num2str(Eps) ', MinPts = ' num2str(MinPts) ')']);toc;​

3 Simulation results

4 reference

[1] Wang Liyu , Sun Bin . Based on improved DBSCAN Research on cloud task scheduling strategy of clustering algorithm [C]// 2016 National communication software Academic Conference . 2016.

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