[MAHOUT-108] Implementation of Assoication Rules learning by Apriori algorithm

• Type: Task
• Status: Closed
• Priority: Major
• Resolution: Won't Fix
• Fix Version/s: 0.2

Environment

Linux, Hadoop-0.17.1

• Assignee: Unassigned
• Reporter: chao deng
• Created: 05/Mar/09 06:17
• Updated: 28/Mar/11 14:45
• Resolved: 14/Sep/09 12:57

Description

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