Implementation of Assoication Rules learning by Apriori algorithm
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Key: MAHOUT-108
URL: https://issues.apache.org/jira/browse/MAHOUT-108
Project: Mahout
Issue Type: Task
Environment: Linux, Hadoop-0.17.1
Reporter: chao deng
Target: Association Rules learning is a popular method for discovering
interesting relations between variables in large databases. Here, we would
implement the Apriori algorithm using Hadoop&Mapreduce parallel techniques.
Applications: Typically, association rules learning is used to discover
regularities between products in large scale transaction data in supermarkets.
For example, the rule "{onions, patatoes}->beef" found in the sales data would
indicate that if a customer buys onions and potatoes together, he or she is
likely to also buy beef. Such information can be used as the basis for
decisions about marketing activities. In addition to the market basket
analysis, association rules are employed today in many application areas
including Web usage mining, intrusion detection and bioinformatics.
Apriori algorithm: Apriori is the best-known algorithm to mine association
rules. It uses a breadth-first search strategy to counting the support of
itemsets and uses a candidate generation function which exploits the downward
closure property of support
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