Write a Python Program to implement FP tree growth Algorithm on a dataset of your own choice.

Sure! Here's an implementation of the FP-growth algorithm in Python using a sample dataset. In this case, I’ll use a simple transactional dataset, which is a common use case for the FP-growth algorithm. The FP-growth algorithm is typically used for frequent itemset mining in a transactional dataset. It works by constructing a frequent pattern tree (FP-tree) and mining the tree for frequent itemsets. The following Python program demonstrates how to implement FP-growth:

```python
from collections import defaultdict

class FPTree:
    def __init__(self, transactions, min_support):
        self.min_support = min_support
        self.frequent_itemsets = {}
        self.tree = self.build_tree(transactions)

    def build_tree(self, transactions):
        header_table = defaultdict(int)

        # Count frequency of items
        for transaction in transactions:
            for item in transaction:
                header_table[item] += 1
        
        # Remove items that don't meet min_support
        header_table = {k: v for k, v in header_table.items() if v >= self.min_support}
        
        if not header_table:
            return None

        # Create root of the FP-tree
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