用matlab实现apriori算法

以下是使用Matlab实现Apriori算法的示例代码：

% 定义事务数据库
transactions = {'A','B','C','D','E';
                'B','C','E';
                'A','B','C','E';
                'A','C','D','E';
                'A','C','E'};

% 定义最小支持度和最小置信度
minSupport = 0.6;
minConfidence = 0.8;

% 第一步：扫描数据库，计算候选项集 C1 的支持度
C1 = {};
for i = 1:size(transactions, 1)
    for j = 1:size(transactions, 2)
        item = transactions{i,j};
        if ~isempty(item) && ~any(strcmp(C1, item))
            C1 = [C1, item];
        end
    end
end

supportData = containers.Map();
for i = 1:length(C1)
    item = C1{i};
    support = sum(cellfun(@(x) any(strcmp(x, item)), transactions)) / length(transactions);
    if support >= minSupport
        supportData(item) = support;
    end
end

% 第二步：生成频繁项集
L = {keys(supportData)};
k = 2;
while ~isempty(L{k-1})
    Ck = {};
    for i = 1:length(L{k-1})
        for j = i+1:length(L{k-1})
            item1 = L{k-1}(i);
            item2 = L{k-1}(j);
            if isequal(item1{1}(1:k-2), item2{1}(1:k-2))
                item = [item1{1}, item2{1}(k-1)];
                if ~any(strcmp(Ck, item))
                    Ck = [Ck, item];
                end
            end
        end
    end
    
    supportDataK = containers.Map();
    for i = 1:length(Ck)
        item = Ck{i};
        support = sum(cellfun(@(x) all(ismember(item, x)), transactions)) / length(transactions);
        if support >= minSupport
            supportDataK(item) = support;
        end
    end
    
    L{k} = keys(supportDataK);
    supportData = [supportData; supportDataK];
    k = k + 1;
end

% 第三步：生成关联规则
rules = {};
for i = 2:length(L)
    for j = 1:length(L{i})
        item = L{i}{j};
        for k = 1:length(item)
            item1 = item(1:k-1);
            item2 = item(k:end);
            confidence = supportData(item) / supportData(item1);
            if confidence >= minConfidence
                rules{end+1} = {item1, item2, confidence};
            end
        end
    end
end

% 输出结果
disp('Frequent itemsets:');
disp(L);
disp('Association rules:');
for i = 1:length(rules)
    rule = rules{i};
    item1 = strjoin(rule{1}, ',');
    item2 = strjoin(rule{2}, ',');
    confidence = rule{3};
    disp([item1, ' ==> ', item2, ', confidence = ', num2str(confidence)]);
end

该代码中的 `transactions` 变量表示事务数据库，其中每行表示一个事务，每列表示事务中的一个项。`minSupport` 和 `minConfidence` 分别表示最小支持度和最小置信度。代码首先扫描数据库，计算候选项集 C1 的支持度，然后生成频繁项集和关联规则。最后输出结果。