【数据聚类】杂草算法优化K-means算法数据聚类【含Matlab源码 2168期】.zip

% Invasive Weed Optimization (IWO) Clustering % Created By Seyed Muhammad Hossein Mousavi - 2022 % Comparison with K-means and GMM clc; clear; close all; warning('off'); %% Basics % Loading data = load('dat'); X = data.XX; % k = 3; % Number of Clusters % CostFunction=@(m) ClusterCost(m, X); % Cost Function VarSize=[k size(X,2)]; % Decision Variables Matrix Size nVar=prod(VarSize); % Number of Decision Variables VarMin= repmat(min(X),k,1); % Lower Bound of Variables VarMax= repmat(max(X),k,1); % Upper Bound of Variables %% IWO Params MaxIt = 25; % Maximum Number of Iterations nPop0 = 2; % Initial Population Size nPop = 5; % Maximum Population Size Smin = 2; % Minimum Number of Seeds Smax = 5; % Maximum Number of Seeds Exponent = 1.5; % Variance Reduction Exponent sigma_initial = 0.2; % Initial Value of Standard Deviation sigma_final = 0.001; % Final Value of Standard Deviation %% Intro % Empty Plant Structure empty_plant.Position = []; empty_plant.Cost = []; empty_plant.Out = []; pop = repmat(empty_plant, nPop0, 1); % Initial Population Array for i = 1:numel(pop) % Initialize Position pop(i).Position = unifrnd(VarMin, VarMax, VarSize); % Evaluation [pop(i).Cost, pop(i).Out]= CostFunction(pop(i).Position); end % Best Solution Ever Found BestSol = pop(1); % Initialize Best Cost History BestCosts = zeros(MaxIt, 1); %% IWO Main Body for it = 1:MaxIt % Update Standard Deviation sigma = ((MaxIt - it)/(MaxIt - 1))^Exponent * (sigma_initial - sigma_final) + sigma_final; % Get Best and Worst Cost Values Costs = [pop.Cost]; BestCost = min(Costs); WorstCost = max(Costs); % Initialize Offsprings Population newpop = []; % Reproduction for i = 1:numel(pop) ratio = (pop(i).Cost - WorstCost)/(BestCost - WorstCost); S = floor(Smin + (Smax - Smin)*ratio); for j = 1:S % Initialize Offspring newsol = empty_plant; % Generate Random Location newsol.Position = pop(i).Position + sigma * randn(VarSize); % Apply Lower/Upper Bounds newsol.Position = max(newsol.Position, VarMin); newsol.Position = min(newsol.Position, VarMax); % Evaluate Offsring [newsol.Cost, newsol.Out] = CostFunction(newsol.Position); % Add Offpsring to the Population newpop = [newpop newsol]; end end % Merge Populations pop = [pop newpop]; % Sort Population [~, SortOrder] = sort([pop.Cost]); pop = pop(SortOrder); % Competitive Exclusion (Delete Extra Members) if numel(pop)>nPop pop = pop(1:nPop); end % Store Best Solution Ever Found BestSol = pop(1); % Store Best Cost History BestCosts(it) = BestSol.Cost; % Display Iteration Information disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCosts(it))]); % Plot DECenters=PlotRes(X, BestSol); pause(0.01); end %% Plot IWO Train figure; semilogy(BestCosts, 'LineWidth', 2); xlabel('Iteration'); ylabel('Best Cost'); grid on; DElbl=BestSol.Out.ind; %% K-Means Clustering for Comparison [kidx,KCenters] = kmeans(X,k); figure set(gcf, 'Position', [150, 50, 700, 400]) subplot(2,3,1) gscatter(X(:,1),X(:,2),kidx);title('K-Means') hold on; plot(KCenters(:,1),KCenters(:,2),'ok','LineWidth',2,'MarkerSize',6); subplot(2,3,2) gscatter(X(:,1),X(:,3),kidx);title('K-Means') hold on; plot(KCenters(:,1),KCenters(:,3),'ok','LineWidth',2,'MarkerSize',6); subplot(2,3,3) gscatter(X(:,1),X(:,4),kidx);title('K-Means') hold on; plot(KCenters(:,1),KCenters(:,4),'ok','LineWidth',2,'MarkerSize',6); subplot(2,3,4) gscatter(X(:,2),X(:,3),kidx);title('K-Means') hold on; plot(KCenters(:,2),KCenters(:,3),'ok','LineWidth',2,'MarkerSize',6); subplot(2,3,5) gscatter(X(:,2),X(:,4),kidx);title('K-Means') hold on; plot(KCenters(:,2),KCenters(:,4),'ok','LineWidth',2,'MarkerSize',6); subplot(2,3,6) gscatter(X(:,3),X(:,4),kidx);title('K-Means') hold on; plot(KCenters(:,3),KCenters(:,4),'ok','LineWidth',2,'MarkerSize',6); % KMeanslbl=kidx; %% Gaussian Mixture Model Clustering for Comparison options = statset('Display','final'); gm = fitgmdist(X,k,'Options',options) idx = cluster(gm,X); figure set(gcf, 'Position', [50, 300, 700, 400]) subplot(2,3,1) gscatter(X(:,1),X(:,2),idx);title('GMM') hold on; subplot(2,3,2) gscatter(X(:,1),X(:,3),idx);title('GMM') hold on; subplot(2,3,3) gscatter(X(:,1),X(:,4),idx);title('GMM') hold on; subplot(2,3,4) gscatter(X(:,2),X(:,3),idx);title('GMM') hold on; subplot(2,3,5) gscatter(X(:,2),X(:,4),idx);title('GMM') hold on; subplot(2,3,6) gscatter(X(:,3),X(:,4),idx);title('GMM') hold on; % GMMlbl=idx; %% MAE and MSE Errors IWO_GMM_MAE=mae(DElbl,GMMlbl); IWO_KMeans_MAE=mae(DElbl,KMeanslbl); GMM_KMeans_MAE=mae(GMMlbl,KMeanslbl); IWO_GMM_MSE=mse(DElbl,GMMlbl); IWO_KMeans_MSE=mse(DElbl,KMeanslbl); GMM_KMeans_MSE=mse(GMMlbl,KMeanslbl); fprintf('IWO vs GMM MAE = %0.4f.\n',IWO_GMM_MAE) fprintf('IWO vs K-Means MAE = %0.4f.\n',IWO_KMeans_MAE) fprintf('GMM vs K-Means MAE = %0.4f.\n',GMM_KMeans_MAE) fprintf('IWO vs GMM MSE = %0.4f.\n',IWO_GMM_MSE) fprintf('IWO vs K-Means MSE = %0.4f.\n',IWO_KMeans_MSE) fprintf('GMM vs K-Means MSE = %0.4f.\n',GMM_KMeans_MSE)