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https://issues.apache.org/jira/browse/MAHOUT-108?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12717167#action_12717167
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chao deng commented on MAHOUT-108:
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Thanks Dunning, this implementation is MapReduce-based parallelized, and it
is new code.
Should i attached it as a zip package?
thanks
2009/6/8 Ted Dunning (JIRA) <[email protected]>
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Name: chao deng
Career: Ph.D candidate on Computer Science
School: Harbin Institue of Technology (HIT)
Department: Computer Science & Technolgy School
Office: Machine Learning Group in Nature Compuation Lab
Mobile:(+86)13836134116
Phone:(+86)045186402407
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Post Address: 319# Harbin Institute of Technology(HIT), P.R. China
> 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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