Finding k-nearest neighbors to get outliers?

i want to implement k-nearest neighbors to get outliers with better time complexity

steps:

1-skip first distance for knn(equal zero).

2-Get mean of every distance point in query .

3-Decceding all mean- distance points to get top n (20) .

4- Get size of real outlier from top n to calculate outlier detection rate.

[Idx,Dist] = knnsearch(Data,query,'k',10,'Distance','cityblock','NSMethod','kdtree','IncludeTies',true);


t=mean(Dist(:,2:end),2);

[distance, index] = sort( t, 'descend');
 out=(query(index(1:20, 1),:));
  [~,ia,~] = intersect(datasetoutlier(:,:),query(:,:),'rows');
nreal-outlier=size(ia);
nout-dataset = size(datasetout(:,:), 1);
detection_rate=nreal-outlier/nout-dataset

1- I want to know if my code is correct to use for big dataset ?.

I found this code in matlab center but i don't know much about parfor .

https://www.mathworks.com/matlabcentral/fileexchange/72244-density-based-outlier-detection-algorithms

2-Does this code will be better performance(run time) because of parfor?

function [id,dist] = matlabKNN(data,k,disMetric)

    if strcmp(disMetric,'euclidean')
        disMetric = 'euclidean';
    else
        error("δ֪µÄ¾àÀë²ÎÊý");
    end
        k = k + 1;

[n,~] = size(data);
    id = zeros(n,k);
    dist = zeros(n,k);
    parfor i = 1:1:n
        datum = data(i,:);
  
        [aID,aDist] = knnsearch(data,datum,'K',k,...
            'Distance',disMetric,'IncludeTies',true);
        aID = aID{1}(1:(k));
        aDist = aDist{1}(1:(k));
        id(i,:) = aID;
        dist(i,:) = aDist;
    end
    
    id1 = id(:,1);
    dist1 = dist(:,1);
    
    id = id(:,2:end);
    dist = dist(:,2:end);
    
    for i = 1:1:n
        mySelf = find(id(i,:) == i);
        if(~isempty(mySelf))
            id(i,mySelf) = id1(i);
            dist(i,mySelf) = dist1(i);
        end
    end
end

thanks