Machine Learning 简明教程
Machine Learning - Association Rules
关联规则挖掘是机器学习中使用的一种技术，用于发现大型数据集中的有趣模式。这些模式以关联规则的形式表示，表示数据集中的不同项目或属性之间的关系。关联规则挖掘的最常见应用是在市场篮子分析中，目的是识别经常一起购买的产品。
Association rule mining is a technique used in machine learning to discover interesting patterns in large datasets. These patterns are expressed in the form of association rules, which represent relationships between different items or attributes in the dataset. The most common application of association rule mining is in market basket analysis, where the goal is to identify products that are frequently purchased together.
关联规则表示为一组前提条件和一组结果。前提条件表示规则适用的条件或项目，而结果表示可能与前提条件相关联的结果或项目。关联规则的强度由两项指标衡量：支持和置信度。支持是在数据集中同时包含前提条件和结果的所有事务的比例，而置信度是包含结果的所有事务中包含前提条件的比例。
Association rules are expressed as a set of antecedents and a set of consequents. The antecedents represent the conditions or items that must be present for the rule to apply, while the consequents represent the outcomes or items that are likely to be associated with the antecedents. The strength of an association rule is measured by two metrics: support and confidence. Support is the proportion of transactions in the dataset that contain both the antecedent and the consequent, while confidence is the proportion of transactions that contain the consequent given that they also contain the antecedent.
Example
在 Python 中，mlxtend 库提供了几种关联规则挖掘函数。以下是使用 mlxtend 中的 apriori 函数在 Python 中实现关联规则挖掘的一个示例 −
In Python, the mlxtend library provides several functions for association rule mining. Here is an example implementation of association rule mining in Python using the apriori function from mlxtend −
import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules
# Create a sample dataset
data = [['milk', 'bread', 'butter'],
['milk', 'bread'],
['milk', 'butter'],
['bread', 'butter'],
['milk', 'bread', 'butter', 'cheese'],
['milk', 'cheese']]
# Encode the dataset
te = TransactionEncoder()
te_ary = te.fit(data).transform(data)
df = pd.DataFrame(te_ary, columns=te.columns_)
# Find frequent itemsets using Apriori algorithm
frequent_itemsets = apriori(df, min_support=0.5, use_colnames=True)
# Generate association rules
rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.5)
# Print the results
print("Frequent Itemsets:")
print(frequent_itemsets)
print("\nAssociation Rules:")
print(rules)在此示例中，我们创建了一个购物交易的样本数据集，并使用 mlxtend 中的 TransactionEncoder 对其进行编码。然后，我们使用 apriori 函数查找最低支持度为 0.5 的频繁项集。最后，我们使用 association_rules 函数生成置信度最低为 0.5 的关联规则。
In this example, we create a sample dataset of shopping transactions and encode it using TransactionEncoder from mlxtend. We then use the apriori function to find frequent itemsets with a minimum support of 0.5. Finally, we use the association_rules function to generate association rules with a minimum confidence of 0.5.
apriori 函数有两个参数：编码数据集和最低支持度阈值。use_colnames 参数设置为 True，以使用原始项目名称，而不是布尔值。association_rules 函数有两个参数：频繁项集和生成关联规则的度量和最低阈值。在此示例中，我们的置信度指标，其最小阈值为 0.5。
The apriori function takes two parameters: the encoded dataset and the minimum support threshold. The use_colnames parameter is set to True to use the original item names instead of Boolean values. The association_rules function takes two parameters: the frequent itemsets and the metric and minimum threshold for generating association rules. In this example, we use the confidence metric with a minimum threshold of 0.5.
Output
此代码的输出将显示频繁项集和生成的关联规则。频繁项集表示数据集中经常同时出现的项目集，而关联规则表示频繁项集中项目的之间的关系。
The output of this code will show the frequent itemsets and the generated association rules. The frequent itemsets represent the sets of items that occur together frequently in the dataset, while the association rules represent the relationships between the items in the frequent itemsets.
Frequent Itemsets:
support itemsets
0 0.666667 (bread)
1 0.666667 (butter)
2 0.833333 (milk)
3 0.500000 (bread, butter)
4 0.500000 (bread, milk)
5 0.500000 (butter, milk)
Association Rules:
antecedents consequents antecedent support consequent support support \
0 (bread) (butter) 0.666667 0.666667 0.5
1 (butter) (bread) 0.666667 0.666667 0.5
2 (bread) (milk) 0.666667 0.833333 0.5
3 (milk) (bread) 0.833333 0.666667 0.5
4 (butter) (milk) 0.666667 0.833333 0.5
5 (milk) (butter) 0.833333 0.666667 0.5
confidence lift leverage conviction zhangs_metric
0 0.75 1.125 0.055556 1.333333 0.333333
1 0.75 1.125 0.055556 1.333333 0.333333
2 0.75 0.900 -0.055556 0.666667 -0.250000
3 0.60 0.900 -0.055556 0.833333 -0.400000
4 0.75 0.900 -0.055556 0.666667 -0.250000
5 0.60 0.900 -0.055556 0.833333 -0.400000关联规则挖掘是一种强大的技术，可以运用到许多不同类型的数据集中。它通常用于市场篮子分析中，以识别经常一起购买的产品，但它也可用于其他领域，例如医疗保健、金融和社交媒体。借助 mlxtend 等 Python 库，可以轻松实现关联规则挖掘，并从大型数据集中生成有价值的见解。
Association rule mining is a powerful technique that can be applied to many different types of datasets. It is commonly used in market basket analysis to identify products that are frequently purchased together, but it can also be applied to other domains such as healthcare, finance, and social media. With the help of Python libraries such as mlxtend, it is easy to implement association rule mining and generate valuable insights from large datasets.