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【数据聚类】基于多元宇宙优化DBSCAN实现数据聚类分析附matlab代码
2022-07-07 09:53:00 【Matlab科研工作室】
1 简介
针对DBSCAN聚类算法对参数敏感,参数选取依靠经验的问题,文章提出了一种基于多元宇宙优化的DBSCAN聚类(MVO-DBSCAN)算法.
2 部分代码
%%% main function:主函数
clc;
clear;
close all;
tic;
% 读取数据
% load('C:\Users\Administrator\Desktop\MATLAb Programming practice\MVO-DBSCAN\X.mat');
load X.mat;
% 数据标签
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
% 待优化参数(宇宙)的上、下界和维度
lb=0.01;
ub=0.5;
dim=1;
% 定义参数MinPts
MinPts =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 Algorithm
Eps=Best_pos;
labels=DBSCAN(X,Eps,MinPts);
figure;
PlotClusterinResult(X, labels);
title(['DBSCAN Clustering (\epsilon = ' num2str(Eps) ', MinPts = ' num2str(MinPts) ')']);
toc;
3 仿真结果
4 参考文献
[1]王李彧, 孙斌. 基于改进的DBSCAN聚类算法的云任务调度策略研究[C]// 2016年全国通信软件学术会议. 2016.
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部分理论引用网络文献,若有侵权联系博主删除。
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