Notification

In the content of this chapter material, you could read section 5.1, 5.2.1 and 5.2.2 to leaning details about the ideas and procedures to mine valid association rules, which are identical to the content Professor Chen introduced to you in class.

Note that you do not need to pay more attention to the algorithm or codes of this method. Instead, ideas and related examples are more important for you to understand this method and it is enough to help you complete the assignment.

Furthermore, to resolve the problem 2.(c) in EXERCISE 3, you need to read section 5.3.1 to know how to do. This part gives you the concept of multi-level association rule or generalized association rule.

基本阅读：英文资料 5.1,5.2.1 和 5.2.2，这部分内容与老师上课所介 绍的内容一致，不必过分专注于其中的算法和代码部分，更重要的是 理解方法意思,过程及其中的相关例子。扩展阅读：为了解决作业问 题 2 中的（c）小问，你还最好阅读 5.3.1 部分。

Mining Frequent Patterns, Associations, and Correlations
Frequent patterns are patterns (such as itemsets, subsequences, or substructures) that appear in a data set frequently. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set is a frequent itemset. A subsequence, such as buying first a PC, then a digital camera, and then a memory card, if it occurs frequently in a shopping history database, is a (frequent) sequential pattern. A substructure can refer to different structural forms, such as subgraphs, subtrees, or sublattices, which may be combined with itemsets or subsequences. If a substructure occurs frequently, it is called a (frequent) structured pattern. Finding such frequent patterns plays an essential role in mining associations, correlations, and many other interesting relationships among data. Moreover, it helps in data classification, clustering, and other data mining tasks as well. Thus, frequent pattern mining has become an important data mining task and a focused