10 comments:

  1. JonathanHahaxJune 30, 2013 at 7:27 AM

    Bro .... Seriously , i Need To Thank You So Damn much ,Also You Saved My Life, My Wifi Card Was Not Enable today and i had a research and i saw your answer to waking it up

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  2. UnknownMay 6, 2014 at 9:13 AM

    hi , Jivan Nepali
    iam trying to implement DBscbut i have error ?? on't know why .

    http://nepalijivan.blogspot.co.uk/p/implementation-of-dbscan-algorithm-in.html

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    Replies
    1. JMay 6, 2014 at 9:47 AM

      Can you please provide me the details of the error and the data set scenario?

      Delete
  3. UnknownMay 7, 2014 at 11:14 AM

    hi, thank you for ur reply

    i just copy past the code , i am trying to implment the data set that you provide :

    function [class,type]=dbscan(x,k,Eps)

    [m,n]=size(x);

    if nargin<3 | isempty(Eps)
    [Eps]=epsilon(x,k);
    end

    x=[[1:m]' x];
    [m,n]=size(x);
    type=zeros(1,m);
    no=1;
    touched=zeros(m,1);
    **** the class=zeros(m,1) is missing ??

    and the complete code is :
    function [class,type]=dbscan(x,k,Eps)

    [m,n]=size(x);

    if nargin<3 || isempty(Eps)
    [Eps]=epsilon(x,k);
    end

    x=[[1:m]' x];
    [m,n]=size(x);
    type=zeros(1,m);
    class=zeros(m,1);
    no=1;
    touched=zeros(m,1);

    for i=1:m
    if touched(i)==0;
    ob=x(i,:);
    D=dist(ob(2:n),x(:,2:n));
    ind=find(D<=Eps);

    if length(ind)>1 && length(ind)=k+1;
    type(i)=1;
    class(ind)=ones(length(ind),1)*max(no);

    while ~isempty(ind)
    ob=x(ind(1),:);
    touched(ind(1))=1;
    ind(1)=[];
    D=dist(ob(2:n),x(:,2:n));
    i1=find(D<=Eps);

    if length(i1)>1
    class(i1)=no;
    if length(i1)>=k+1;
    type(ob(1))=1;
    else
    type(ob(1))=0;
    end

    for i=1:length(i1)
    if touched(i1(i))==0
    touched(i1(i))=1;
    ind=[ind i1(i)];
    class(i1(i))=no;
    end
    end
    end
    end
    no=no+1;
    end
    end
    end

    i1=find(class==0);
    class(i1)=-1;
    type(i1)=-1;

    function [Eps]=epsilon(x,k)

    % Function: [Eps]=epsilon(x,k)
    %
    % Aim:
    % Analytical way of estimating neighborhood radius for DBSCAN
    %
    % Input:
    % x - data matrix (m,n); m-objects, n-variables
    % k - number of objects in a neighborhood of an object
    % (minimal number of objects considered as a cluster)

    [m,n]=size(x);

    Eps=((prod(max(x)-min(x))*k*gamma(.5*n+1))/(m*sqrt(pi.^n))).^(1/n);


    %............................................

    function [D]=dist(i,x)

    % function: [D]=dist(i,x)
    %
    % Aim:
    % Calculates the Euclidean distances between the i-th object and all objects in x
    %
    % Input:
    % i - an object (1,n)
    % x - data matrix (m,n); m-objects, n-variables
    %
    % Output:
    % D - Euclidean distance (m,1)

    [m,n]=size(x);
    D=sqrt(sum((((ones(m,1)*i)-x).^2)'));

    if n==1
    D=abs((ones(m,1)*i-x))';
    end

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  4. UnknownMay 7, 2014 at 11:17 AM

    i saved the the file dbscan.m usually we saved the matlab file with first function name

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  5. UnknownMay 7, 2014 at 11:21 AM

    1- the error are function key word use is invalid here
    2- x=[[1:m]' x]; use of bracket unnecessary

    3- D=sqrt(sum((((ones(m,1)*i)-x).^2)' using transpose has error

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  6. UnknownMay 7, 2014 at 11:22 AM

    could you please guide me

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  7. UnknownMay 8, 2014 at 12:49 AM

    This comment has been removed by the author.

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  8. UnknownJune 9, 2014 at 6:17 AM

    hi, you did not reply when i asked you about the algorithm , i runed the algorithm but its not cluster the data in the i was expecting its not determining the dense region ??

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