基于关联规则的FP-Growth算法的matlab代码

以下是基于关联规则的FP-Growth算法的Matlab代码：

% 输入数据为包含事务数据的矩阵或表格，minsup为支持度阈值，minconf为置信度阈值
% 输出频繁项集及其支持度，以及关联规则及其置信度
function [freqItemsets, assocRules] = fpGrowthAssoc(data, minsup, minconf)
    % 将数据转换为二进制矩阵，其中1表示该项出现，0表示该项未出现
    binData = double(data > 0);
    % 计算事务数和项数
    [numTrans, numItems] = size(binData);
    % 初始化FP树
    tree = struct('item', [], 'count', 0, 'parent', [], 'children', []);
    headerTable = repmat(struct('item', [], 'count', 0, 'nodeLink', []), numItems, 1);
    for i = 1:numTrans
        trans = find(binData(i, :));
        tree = insertTree(tree, headerTable, trans, 1);
    end
    % 构建频繁项集
    freqItemsets = struct('itemset', [], 'support', []);
    freqItemsets = findFreqItemsets(tree, headerTable, minsup, [], freqItemsets);
    % 构建关联规则
    assocRules = struct('antecedent', [], 'consequent', [], 'confidence', []);
    for i = 1:length(freqItemsets)
        if length(freqItemsets(i).itemset) > 1
            rules = findAssocRules(tree, headerTable, freqItemsets(i).itemset, minconf);
            assocRules = [assocRules; rules];
        end
    end
end

% 向FP树中插入一条事务
function tree = insertTree(tree, headerTable, trans, count)
    if isempty(trans)
        return
    end
    item = trans(1);
    childIdx = findItemIdx(tree.children, item);
    if isempty(childIdx)
        % 如果该项在当前节点的子节点中不存在，则创建一个新的子节点并插入
        newChild = struct('item', item, 'count', count, 'parent', tree, 'children', []);
        tree.children = [tree.children, newChild];
        % 更新头指针表
        headerIdx = findItemIdx(headerTable, item);
        if isempty(headerTable(headerIdx).nodeLink)
            headerTable(headerIdx).nodeLink = newChild;
        else
            curNode = headerTable(headerIdx).nodeLink;
            while ~isempty(curNode.nodeLink)
                curNode = curNode.nodeLink;
            end
            curNode.nodeLink = newChild;
        end
        % 递归插入剩余项
        tree = insertTree(newChild, headerTable, trans(2:end), count);
    else
        % 如果该项在当前节点的子节点中存在，则更新计数并递归插入剩余项
        childNode = tree.children(childIdx);
        childNode.count = childNode.count + count;
        tree.children(childIdx) = childNode;
        tree = insertTree(childNode, headerTable, trans(2:end), count);
    end
end

% 查找频繁项集
function freqItemsets = findFreqItemsets(tree, headerTable, minsup, prefix, freqItemsets)
    % 如果当前节点的计数大于等于支持度阈值，则将其加入频繁项集
    if tree.count >= minsup
        freqItemset = struct('itemset', [prefix, tree.item], 'support', tree.count);
        freqItemsets = [freqItemsets, freqItemset];
    end
    % 对于每个项头表中的项，构建条件模式基并递归查找频繁项集
    for i = 1:length(headerTable)
        headerNode = headerTable(i).nodeLink;
        if ~isempty(headerNode)
            prefixPath = prefix;
            freqItem = headerTable(i).item;
            freqItemset = struct('itemset', [prefix, freqItem], 'support', headerTable(i).count);
            freqItemsets = [freqItemsets, freqItemset];
            while ~isempty(headerNode)
                prefixPath = [prefixPath, freqItem];
                condBase = [];
                curNode = headerNode;
                while ~isempty(curNode.parent)
                    condBase = [curNode.item, condBase];
                    curNode = curNode.parent;
                end
                for j = 1:curNode.count
                    condBase = [freqItem, condBase];
                end
                condTree = struct('item', [], 'count', 0, 'parent', [], 'children', []);
                condHeaderTable = repmat(struct('item', [], 'count', 0, 'nodeLink', []), length(headerTable), 1);
                for j = 1:size(condBase, 2)
                    condTree = insertTree(condTree, condHeaderTable, condBase(j), 1);
                end
                freqItemsets = findFreqItemsets(condTree, condHeaderTable, minsup, prefixPath, freqItemsets);
                headerNode = headerNode.nodeLink;
            end
        end
    end
end

% 查找关联规则
function rules = findAssocRules(tree, headerTable, itemset, minconf)
    rules = struct('antecedent', [], 'consequent', [], 'confidence', []);
    if length(itemset) == 1
        return
    end
    % 构建项子集
    subsets = generateSubsets(itemset);
    % 对于每个项子集，计算置信度并加入关联规则
    for i = 1:size(subsets, 1)
        antecedent = subsets(i, :);
        consequent = setdiff(itemset, antecedent);
        antecedentIdx = findItemIdx(headerTable, antecedent(end));
        consequentIdx = findItemIdx(headerTable, consequent(end));
        antecedentSupport = headerTable(antecedentIdx).count;
        consequentSupport = headerTable(consequentIdx).count;
        itemsetSupport = tree.count;
        confidence = itemsetSupport / antecedentSupport;
        if confidence >= minconf
            rule = struct('antecedent', antecedent, 'consequent', consequent, 'confidence', confidence);
            rules = [rules, rule];
        end
    end
end

% 查找项在数组中的索引
function idx = findItemIdx(array, item)
    idx = find(array == item, 1);
end

% 生成项子集
function subsets = generateSubsets(itemset)
    subsets = [];
    for i = 1:length(itemset)
        subsets = [subsets; nchoosek(itemset, i)];
    end
end

该代码实现了基于关联规则的FP-Growth算法，可以通过输入数据、支持度阈值和置信度阈值来输出频繁项集及其支持度，以及关联规则及其置信度。其中，insertTree函数用于向FP树中插入一条事务；findFreqItemsets函数用于查找频繁项集；findAssocRules函数用于查找关联规则；其他函数则用于辅助实现算法。