Apriori关联分析 matlab代码

对于Apriori关联分析的MATLAB代码，可以使用以下代码示例：

% 数据集示例
dataset = {'A', 'B', 'C', 'D';
           'A', 'C', 'D';
           'B', 'D';
           'A', 'C', 'E';
           'A', 'C', 'D', 'E';
           'B', 'C', 'E'};

% 最小支持度阈值
minSupport = 0.4;

% 最小置信度阈值
minConfidence = 0.6;

% 使用Apriori算法进行关联分析
result = RuleFinder(dataset, minSupport, minConfidence);

% 输出关联规则
disp('关联规则：');
for i = 1:length(result)
    disp(result(i).rule);
end

function result = RuleFinder(dataset, minSupport, minConfidence)
    % 获取所有可能项集
    uniqueItems = unique([dataset{:}]);
    
    % 构建初始候选项集
    candidateItemsets = num2cell(uniqueItems);
    
    % 计算初始候选项集的支持度
    supportCounts = CountSupport(candidateItemsets, dataset);
    
    % 生成频繁项集
    frequentItemsets = candidateItemsets(supportCounts >= minSupport);
    
    % 初始化结果
    result = struct('rule', [], 'support', [], 'confidence', []);
    
    % 生成关联规则
    for i = 1:length(frequentItemsets)
        itemset = frequentItemsets{i};
        
        % 生成候选规则
        rules = GenerateRules(itemset);
        
        % 计算规则的支持度和置信度
        for j = 1:length(rules)
            rule = rules{j};
            antecedent = rule{1};
            consequent = rule{2};
            
            support = CountSupport([antecedent, consequent], dataset);
            confidence = support / CountSupport(antecedent, dataset);
            
            % 添加符合条件的关联规则到结果中
            if confidence >= minConfidence
                result(end+1).rule = rule;
                result(end).support = support;
                result(end).confidence = confidence;
            end
        end
    end
end

function supportCounts = CountSupport(itemsets, dataset)
    supportCounts = zeros(size(itemsets));
    
    for i = 1:length(itemsets)
        itemset = itemsets{i};
        
        for j = 1:size(dataset, 1)
            if all(ismember(itemset, dataset(j, :)))
                supportCounts(i) = supportCounts(i) + 1;
            end
        end
    end
end

function rules = GenerateRules(itemset)
    rules = {};
    
    for i = 1:length(itemset)
        antecedent = itemset(i);
        consequent = setdiff(itemset, antecedent);
        
        if ~isempty(consequent)
            rules{end+1} = {antecedent, consequent};
        end
    end
end

这是一个基本的Apriori关联分析的MATLAB代码示例。你可以根据自己的数据集和需求进行相应的修改和调整。