# Efficient False Positive Control Algorithms in Big Data Mining
## 2 Related Concepts and Techniques
### Pattern Mining-Related Techniques  
Frequent pattern mining [34] is one of the most widely studied problems in data mining. Compared to deep learning, which gradually transforms the initial \"low-level\" feature representation into a \"high-level\" feature representation through multi-layer processing by simulated neural networks [35; 36], and completes complex classification and other learning tasks with a \"simple model\", frequent pattern mining is a key step of association rule mining in data mining. Frequent patterns generally refer to the set of items that occur with high frequency in a dataset. For example, items that appear frequently in the shopping basket dataset (e.g., toothbrush and toothpaste) can form a frequent item set. For a sequence in the shopping basket database (e.g., first buy flour, then eggs, then basin), it is said to be a frequent sequence if it appears frequently in the shopping cart data. Commonly used frequent pattern mining algorithms include Apriori, FP-Growth, and others.  
The Apriori algorithm [37] is a commonly used pattern mining algorithm. The algorithm usually uses prior knowledge in its process. The core idea of the Apriori algorithm for performing pattern mining is that if an item set is a frequent item set, then all its subsets are also frequent, i.e., if {toothbrush,toothpaste} is frequent, then {toothpush}, {toothpaste} must also be frequent, and if {insoles} is not a frequent item set then its superset {shoes,insoles} must also not be a frequent item set.  
The Apriori algorithm uses an iterative approach to computation. The algorithm uses the k-item set in the process of finding frequent patterns for the \(k+1\)-item set. The specific implementation steps of the Apriori algorithm are: traversing the dataset, obtaining the count of each item, and determining the support of each item. The set of all items that satisfy the minimum support is the frequent 1-item set. The frequent 1-item set is then used to find the frequent 2-item set, and so on, until all frequent patterns are found.  
The FP-Growth algorithm [38] is a frequent item set mining method proposed by [PERSON], which stores the items in the dataset sorted by support degree in FP-Tree and labels the support degree of each node; it mines frequent item sets according to FP-Tree.  
The FP-Growth algorithm is implemented in the following steps: first, the dataset is scanned, and the purpose of this operation is to prepare for the construction of the item header table. While scanning, the items with support greater than the minimum support threshold are constructed as frequent item sets and then arranged in descending order of support; secondly, the dataset is scanned again to create the item head table and FP tree in descending order of support. After the creation of the head table and the FP tree, the pattern mining operation is performed. This recursively invokes the tree structure to construct the conditional pattern base for each item in each node of the item header table. The conditional pattern base for each item is the set of all paths from the root node to that item in the conditional tree. If the resulting tree structure is a single path, the recursion ends by enumerating all combinations; if a non-single path is obtained, the tree structure continues to be invoked until a single path is formed.