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From "Ted Dunning (JIRA)" <j...@apache.org>
Subject [jira] Commented: (MAHOUT-108) Implementation of Assoication Rules learning by Apriori algorithm
Date Mon, 08 Jun 2009 06:02:07 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-108?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12717161#action_12717161
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Ted Dunning commented on MAHOUT-108:
------------------------------------

Probably the best thing to do is to attach a patch to this JIRA so that it can be reviewed.

One question, I have right away is whether this implementation is sequential or is parallelized.
 Also, is this new code or is it based on other code? 

> Implementation of Assoication Rules learning by Apriori algorithm
> -----------------------------------------------------------------
>
>                 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
>             Fix For: 0.2
>
>   Original Estimate: 504h
>  Remaining Estimate: 504h
>
> 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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