FP-Growth算法的python实现

FP-Growth算法是一种高效的频繁项集挖掘算法，可以用于数据挖掘、关联规则挖掘等领域。下面是FP-Growth算法的Python实现：

class FPTree:
    def __init__(self, item, count, parent):
        self.item = item
        self.count = count
        self.parent = parent
        self.children = {}
        self.next = None

    def increment(self, count):
        self.count += count

    def display(self, indent=1):
        print('  ' * indent, self.item, ' ', self.count)
        for child in self.children.values():
            child.display(indent + 1)

def build_fptree(transactions, min_support):
    counts = {}
    for transaction in transactions:
        for item in transaction:
            if item in counts:
                counts[item] += 1
            else:
                counts[item] = 1
    items = [item for item in counts.keys() if counts[item] >= min_support]
    items.sort(key=lambda item: counts[item], reverse=True)
    root = FPTree(None, 0, None)
    for transaction in transactions:
        transaction = [item for item in transaction if item in items]
        transaction.sort(key=lambda item: counts[item], reverse=True)
        current = root
        for item in transaction:
            if item in current.children:
                child = current.children[item]
                child.increment(1)
            else:
                child = FPTree(item, 1, current)
                current.children[item] = child
            current = child
    return root, items, counts

def find_prefix_path(item, node):
    paths = []
    while node is not None:
        path = []
        while node.item is not None and node.item != item:
            path.append(node.item)
            node = node.parent
        if node is not None:
            path.append(node.item)
            paths.append(path[::-1])
        node = node.parent
    return paths

def mine_frequent_itemsets(fptree, items, min_support, prefix=[]):
    frequent_itemsets = []
    for item in items:
        support = sum([1 for path in find_prefix_path(item, fptree) if len(path) >= len(prefix) + 1])
        if support >= min_support:
            frequent_itemset = prefix + [item]
            frequent_itemsets.append((frequent_itemset, support))
            conditional_tree, conditional_items, conditional_counts = build_fptree(
                [path for path in find_prefix_path(item, fptree) if len(path) >= len(prefix) + 1], min_support)
            if conditional_tree is not None:
                frequent_itemsets.extend(mine_frequent_itemsets(conditional_tree, conditional_items,
                                                                 min_support, frequent_itemset))
    return frequent_itemsets

其中，FPTree类表示FP树，包含了项、计数、父节点、子节点和链表指针等信息。build_fptree函数用于构建FP树，输入参数为事务列表和最小支持度，输出结果为FP树、频繁项和项计数。find_prefix_path函数用于寻找所有包含指定项的前缀路径，输入参数为项和节点，输出结果为前缀路径列表。mine_frequent_itemsets函数用于挖掘频繁项集，输入参数为FP树、频繁项、最小支持度和前缀，输出结果为频繁项集列表。