Detailed explanation of association rules apriori algorithm

Association rules are an important technology in data mining, which are used to discover the association between items in the data set. Algorithm steps: 1. The algorithm needs to initialize a candidate item set containing all single items; 2. The algorithm will generate a candidate item set based on frequent item sets; 3. The algorithm will prune the candidate item set; 4. The algorithm is satisfied The required candidate item sets will then be used as new frequent item sets and enter the next round of iteration; 5. When the iteration ends, the algorithm will obtain all frequent item sets that meet the set threshold. Association rules are then generated based on frequent itemsets.

Association rules are an important technology in data mining, which are used to discover the association between items in the data set. The association rule apriori algorithm is a commonly used algorithm for mining association rules. The principles and steps of the association rule apriori algorithm will be introduced in detail below.

Algorithm principle

The association rule apriori algorithm is based on two key concepts: support and confidence. Support represents the frequency of an item set appearing in the data, while confidence represents the reliability of the rule. The core idea of the algorithm is to generate candidate item sets from frequent item sets through iteration, calculate support and confidence, and finally find association rules that meet the set threshold.

Algorithm steps

The steps of the association rule apriori algorithm are as follows:

Initialization

First, the algorithm needs to initialize an algorithm containing all single The set of candidate items for the item. These itemsets are called 1-itemsets. The algorithm then scans the data set and calculates the support of each 1-item set.

Generate candidate item sets

Through iteration, the algorithm will generate candidate item sets based on frequent item sets. Frequent itemsets refer to itemsets whose support is greater than or equal to the set threshold. Assuming that the frequent item set of the current iteration is a k-item set, then by taking the union of the k-item sets and removing duplicate items, a k 1-item set can be generated. The algorithm then scans the data set and calculates the support for each k 1-itemset.

Pruning

After generating the candidate item set, the algorithm will prune the candidate item set. If a subset of a candidate itemset is not a frequent itemset, then the candidate itemset cannot be a frequent itemset. Therefore, the algorithm deletes these candidate item sets that do not meet the requirements.

Update frequent itemsets

Through pruning operation, the algorithm obtains candidate item sets that meet the requirements. Then, the algorithm will use these candidate itemsets as new frequent itemsets and enter the next round of iteration.

Generate association rules

When the iteration ends, the algorithm will obtain all frequent item sets that meet the set threshold. The algorithm then generates association rules based on frequent itemsets. Association rules are generated by calculating confidence. For a frequent itemset, multiple association rules can be generated. The association rules are in the form A->B, where A and B are subsets of frequent itemsets respectively.

Algorithm optimization

The association rule apriori algorithm may face the problem of high computational complexity when processing large-scale data sets. In order to reduce the computational complexity, the following optimization measures can be adopted:

Compressed data set

You can compress the data set to delete non-frequent item sets in the data set, thereby reducing the amount of calculation.

Using Hash Table

You can use a hash table to store frequent itemsets, thereby improving the efficiency of search.

Transaction database

The data set can be converted into the form of a transaction database, and each transaction represents an item set. This can reduce the number of times the data set is scanned and improve the efficiency of the algorithm.

To sum up, the association rule apriori algorithm is a commonly used algorithm for mining association rules. Through iteration, candidate item sets are generated from frequent item sets, support and confidence are calculated, and association rules that meet the set threshold are finally found. In order to reduce the computational complexity, optimization measures such as compressing the data set, using hash tables and transaction databases can be used.

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