26-04-2016, 10:43 AM
What is Apriori?
Apriori is an algorithm for discovering frequent itemsets in transaction databases. It was proposed by Agrawal & Srikant (1993).
What is the input of the Apriori algorithm?
The input is a transaction database (aka binary context) and a threshold named minsup (a value between 0 and 100 %).
A transaction database is a set of transactions. Each transaction is a set of items. For example, consider the following transaction database. It contains 5 transactions (t1, t2, ..., t5) and 5 items (1,2, 3, 4, 5). For example, the first transaction represents the set of items 1, 3 and 4. This database is provided as the file contextPasquier99.txt in the SPMF distribution. It is important to note that an item is not allowed to appear twice in the same transaction and that items are assumed to be sorted by lexicographical order in a transaction.
What is the output of the Apriori algorithm?
Apriori is an algorithm for discovering itemsets (group of items) occurring frequently in a transaction database (frequent itemsets). A frequent itemset is an itemset appearing in at least minsup transactions from the transaction database, where minsup is a parameter given by the user.
In data mining, Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation).
Other algorithms are designed for finding association rules in data having no transactions (Winepi and Minepi), or having no timestamps (DNA sequencing).
The whole point of the algorithm (and data mining, in general) is to extract useful information from large amounts of data. For example, the information that a customer who purchases a keyboard also tends to buy a mouse at the same time is acquired from the association rule below:
Support: The percentage of task-relevant data transactions for which the pattern is true.
Support (Keyboard -> Mouse) = AprioriAlgorithm/eq_1.JPG
Confidence: The measure of certainty or trustworthiness associated with each discovered pattern.
Confidence (Keyboard -> Mouse) = AprioriAlgorithm/eq_2.JPG
The algorithm aims to find the rules which satisfy both a minimum support threshold and a minimum confidence threshold (Strong Rules).
Item: article in the basket.
Itemset: a group of items purchased together in a single transaction.
How Apriori Works
Find all frequent itemsets:
Get frequent items:
Items whose occurrence in database is greater than or equal to the min.support threshold.
Get frequent itemsets:
Generate candidates from frequent items.
Prune the results to find the frequent itemsets.
Generate strong association rules from frequent itemsets
Rules which satisfy the min.support and min.confidence threshold.
Apriori is an algorithm for discovering frequent itemsets in transaction databases. It was proposed by Agrawal & Srikant (1993).
What is the input of the Apriori algorithm?
The input is a transaction database (aka binary context) and a threshold named minsup (a value between 0 and 100 %).
A transaction database is a set of transactions. Each transaction is a set of items. For example, consider the following transaction database. It contains 5 transactions (t1, t2, ..., t5) and 5 items (1,2, 3, 4, 5). For example, the first transaction represents the set of items 1, 3 and 4. This database is provided as the file contextPasquier99.txt in the SPMF distribution. It is important to note that an item is not allowed to appear twice in the same transaction and that items are assumed to be sorted by lexicographical order in a transaction.
What is the output of the Apriori algorithm?
Apriori is an algorithm for discovering itemsets (group of items) occurring frequently in a transaction database (frequent itemsets). A frequent itemset is an itemset appearing in at least minsup transactions from the transaction database, where minsup is a parameter given by the user.
In data mining, Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation).
Other algorithms are designed for finding association rules in data having no transactions (Winepi and Minepi), or having no timestamps (DNA sequencing).
The whole point of the algorithm (and data mining, in general) is to extract useful information from large amounts of data. For example, the information that a customer who purchases a keyboard also tends to buy a mouse at the same time is acquired from the association rule below:
Support: The percentage of task-relevant data transactions for which the pattern is true.
Support (Keyboard -> Mouse) = AprioriAlgorithm/eq_1.JPG
Confidence: The measure of certainty or trustworthiness associated with each discovered pattern.
Confidence (Keyboard -> Mouse) = AprioriAlgorithm/eq_2.JPG
The algorithm aims to find the rules which satisfy both a minimum support threshold and a minimum confidence threshold (Strong Rules).
Item: article in the basket.
Itemset: a group of items purchased together in a single transaction.
How Apriori Works
Find all frequent itemsets:
Get frequent items:
Items whose occurrence in database is greater than or equal to the min.support threshold.
Get frequent itemsets:
Generate candidates from frequent items.
Prune the results to find the frequent itemsets.
Generate strong association rules from frequent itemsets
Rules which satisfy the min.support and min.confidence threshold.
