FP-Growth（Frequent Pattern Growth）是一种用于发现频繁项集的数据挖掘算法，通常用于关联规则挖掘。下面是一个简单的Python实现FP-Growth算法的示例：
```python from collections import defaultdict class FPNode: def __init__(self, item, count, parent): self.item = item self.count = count self.parent = parent self.children = {} self.next = None def increment_count(self, count): self.count += count def build_tree(transactions, min_support): header_table = defaultdict(int) for transaction in transactions: for item in transaction: header_table[item] += 1 header_table = {k: v for k, v in header_table.items() if v >= min_support} if len(header_table) == 0: return None, None root = FPNode(None, 0, None) for transaction in transactions: transaction = [item for item in transaction if item in header_table] transaction.sort(key=lambda item: header_table[item], reverse=True) current_node = root for item in transaction: if item in current_node.children: current_node.children[item].increment_count(1) else: new_node = FPNode(item, 1, current_node) current_node.children[item] = new_node if header_table[item] == 1: update_fp_tree(new_node, header_table) current_node = current_node.children[item] return root, header_table def update_fp_tree(node, header_table): while node.next is not None: node = node.next node.next = header_table[node.item] def find_frequent_patterns(tree, header_table, prefix, frequent_patterns, min_support): for item, count in header_table.items(): new_prefix = prefix.copy() new_prefix.add(item) frequent_patterns.add(frozenset(new_prefix)) conditional_patterns = get_conditional_patterns(item, header_table) conditional_tree, conditional_header = build_tree(conditional_patterns, min_support) if conditional_tree is not None: find_frequent_patterns(conditional_tree, conditional_header, new_prefix, frequent_patterns, min_support) def get_conditional_patterns(item, header_table): conditional_patterns = [] node = header_table[item] while node is not None: prefix_path = [] current_node = node.parent while current_node.item is not None: prefix_path.append(current_node.item) current_node = current_node.parent if len(prefix_path) > 0: conditional_patterns.append(prefix_path) node = node.next return conditional_patterns def fp_growth(transactions, min_support): tree, header_table = build_tree(transactions, min_support) frequent_patterns = set() find_frequent_patterns(tree, header_table, set(), frequent_patterns, min_support) return frequent_patterns # 示例数据 transactions = [ ['A', 'B', 'D'], ['B', 'C', 'E'], ['A', 'B', 'D', 'E'], ['A', 'B', 'C', 'E'] ] min_support = 2 frequent_patterns = fp_growth(transactions, min_support) for pattern in frequent_patterns: print(pattern) ```
这是一个简单的FP-Growth算法的Python实现示例。您可以根据需要进行调整和扩展。这段代码可以帮助您理解FP-Growth算法的基本原理和实现方式。
