【数据聚类】基于蝙蝠算法实现数据聚类附matlab代码

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⛄ 内容介绍

聚类分析是近年来迅速发展起来的一种新兴的数据处理技术,它在许多领域有着广泛的应用,尤其是在数据挖掘领域.本文实现基于蝙蝠算法实现数据聚类。

⛄ 部分代码

%%-------------------------------------------------------------------------

% (Citation details):                                                    % 

% J. Senthilnath, Sushant Kulkarni, J.A. Benediktsson and X.S. Yang      %

% (2016) "",        %

% IEEE Letters for Geoscience and Remote Sensing Letters,                %

%  Vol. 13, No. 4, pp.599?03.                                           %

%%-------------------------------------------------------------------------

function [best]=Bat_Algorithm(traindat,limits,v)

% Default parameters

n= 5;                                 % Population size, typically 10 to 40

N_gen= 50;                       % Number of generations

% Iteration parameters

A=zeros(n,1);                    % Loudness  (constant or decreasing)

r= zeros(n,1);                    % Pulse rate (constant or decreasing)

% The frequency range determines the scalings

% These values need to be changed as necessary

Qmin=0;                           % Frequency minimum

Qmax=2;                          % Frequency maximum

% Dimension of the search variables

d=v;                                  % Number of dimensions 

N_iter=0;                           % Total number of function eval（1,:);

% Lower limit/bounds/ a vector

Lb=limits(2,:);

% Initializing arrays

Q=zeros(n,1);                     % Frequency of Bats

v=zeros(n,d);                      % Velocities of Bats

% Initialize the population/solutions

for i=1:n,

  Sol(i,:)=Lb+(Ub-Lb).*rand(1,d);

  Fitness(i)=Fun(Sol(i,:));

   r(i)=rand(1);

   A(i)=1+rand(1);

end

r0=r;

plot(Sol(:,1),Sol(:,2),'gs', 'LineWidth',1.5);     % plot initial solutions for visualization

hold on;

% Find the initial best solution

% Here, probable center with least distance in cluster

[fmin, I]=min(Fitness);

best=Sol(I,:);

% Start of iterations -- Bat Algorithm (essential part)  %

for t=1:N_gen

        % Loop over all bats/solutions

        for i=1:n

            Q(i)=Qmin+(Qmax-Qmin)*rand;

            v(i,:)=v(i,:)+(Sol(i,:)-best)*Q(i);

            S(i,:)=Sol(i,:)+v(i,:);

            tem(1,:) = Sol(i,:);                  % Solution before movement

            % Apply simple bounds/limits

            S(i,:)=simplebounds(S(i,:),Lb,Ub);

            % Pulse rate

            if rand>r(i)

                % The factor 0.001 limits the step sizes of random walks 

                S(i,:)=S(i,:)+0.001*randn(1,d);

                S(i,:)=simplebounds(S(i,:),Lb,Ub);

            end

           % Evaluate new solutions

           Fnew=Fun(S(i,:));

           % Update if the solution improves, or not too loud

           if (Fnew<=Fitness(i)) && (rand<A(i)) 

                Sol(i,:)=S(i,:);                   % Replace initial solution with improvised solution

                tem(2,:) = S(i,:);                % Solution after movement

                Fitness(i)=Fnew;              % Replace initial fitness with improvised fitness

                A(i)=0.9*A(i);                  % Update the Loudness of Bats

                r(i)=r0(i)*(1-exp(-0.9*N_gen)); % Update the Pitch of Bats

           end

          % Find and update the current best solution

          if Fnew<=fmin,

                best=S(i,:);

                fmin=Fnew;

          end

        % plot the movement of the solutions

        pause(0.005)

        hold on;

        plot(tem(:,1),tem(:,2),'k:');  

        end

        N_iter=N_iter+n;

end

% plot the final optimal cluster center

plot(best(1),best(2),'k*', 'LineWidth',3) 

legend('Class 1 Training','Class 2 Training','Class 1 Testing','Class 2 Testing ','Agents','Agents Movement','Location','NorthEastOutside')

text(33,23,'* Cluster Centers', 'FontName','Times','Fontsize',12)

% Output/display

disp(['Number of eval（N_iter)]);

disp(['Best =',num2str(best),' fmin=',num2str(fmin)]);

% Application of simple limits/bounds

    function s=simplebounds(s,Lb,Ub)

        % Apply the lower bound vector

        ns_tmp=s;

        tt=ns_tmp<Lb;

        ns_tmp(tt)=Lb(tt);

        % Apply the upper bound vector 

        J=ns_tmp>Ub;

        ns_tmp(J)=Ub(J);

        % Update this new move 

        s=ns_tmp;

     end

⛄ 运行结果

⛄ 参考文献

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