
    Design a function that determines the number of possible ways to categorize suspicious banking transactions into distinct behavioral patterns for fraud detection. Given n different transaction patterns observed in the banking system, calculate how many different ways these patterns can be grouped into non-empty subsets to create comprehensive fraud detection rules. The groupings must collectively cover all observed patterns without any pattern being unused or appearing in multiple groups.
    

    
def bell_number(n):
    """
    Design a function that determines the number of possible ways to categorize suspicious banking transactions into distinct behavioral patterns for fraud detection. Given n different transaction patterns observed in the banking system, calculate how many different ways these patterns can be grouped into non-empty subsets to create comprehensive fraud detection rules. The groupings must collectively cover all observed patterns without any pattern being unused or appearing in multiple groups.
    

    """
def bell_number(n):
    """
    Design a function that determines the number of possible ways to categorize suspicious banking transactions into distinct behavioral patterns for fraud detection. Given n different transaction patterns observed in the banking system, calculate how many different ways these patterns can be grouped into non-empty subsets to create comprehensive fraud detection rules. The groupings must collectively cover all observed patterns without any pattern being unused or appearing in multiple groups.
    

    """
    bell = [[0 for i in range(n + 1)] for j in range(n + 1)]
    bell[0][0] = 1
    for i in range(1, n + 1):
        bell[i][0] = bell[i - 1][i - 1]
        for j in range(1, i + 1):
            bell[i][j] = bell[i - 1][j - 1] + bell[i][j - 1]
    return bell[n][0]