购物篮Apriori算法的频繁项集产生matlab代码

以下是使用MATLAB实现Apriori算法的代码，用于生成频繁项集：

function [F, items] = freq_itemset_apriori(data, minsup)
% data: a cell array of transactions, each transaction is a row vector of
%       item IDs.
% minsup: minimum support threshold

% Get all unique items in the dataset
items = unique([data{:}]);
n = numel(data);

% Initialize frequent itemset
F{1} = items(:);

% Generate frequent itemsets of length > 1
k = 2;
while ~isempty(F{k-1})
    % Generate candidate itemsets
    C = apriori_gen(F{k-1});
    
    % Count support of candidate itemsets
    sup = count_support(C, data);
    
    % Prune infrequent itemsets
    F{k} = C(sup >= minsup, :);
    
    k = k + 1;
end

% Remove empty itemsets
F = F(1:end-1);

end


function C = apriori_gen(F)
% Generate candidate itemsets of length k+1 from frequent itemsets of
% length k.

n = size(F, 2);
C = {};

for i = 1:n-1
    for j = i+1:n
        % Check if the first k-1 items are the same
        if all(F(1:end-1,i) == F(1:end-1,j))
            % Add the union of the two itemsets
            C{end+1} = sort([F(:,i); F(:,j)]);
        end
    end
end

% Remove duplicates
C = unique(C);

end


function sup = count_support(C, data)
% Count the support of candidate itemsets in the dataset

n = numel(data);
m = numel(C);
sup = zeros(m, 1);

for i = 1:m
    itemset = C{i};
    for j = 1:n
        % Check if the itemset is a subset of the transaction
        if all(ismember(itemset, data{j}))
            sup(i) = sup(i) + 1;
        end
    end
end

end

请注意，此代码仅生成频繁项集，而不是关联规则。要生成关联规则，您需要编写另一个函数来计算每个频繁项集的关联规则，并选择满足最小置信度阈值的规则。