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https://issues.apache.org/jira/browse/MAHOUT-173?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12796538#action_12796538
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Ted Dunning commented on MAHOUT-173:
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It seems that this algorithm is a combination of a hashed kernel and k-means
clustering.
Would it be easiest to implement this using the vectorization algorithms
described as part of MAHOUT-228 and then just using the normal k-means
algorithm?
> Implement clustering of massive-domain attributes
> -------------------------------------------------
>
> Key: MAHOUT-173
> URL: https://issues.apache.org/jira/browse/MAHOUT-173
> Project: Mahout
> Issue Type: New Feature
> Components: Clustering
> Affects Versions: 0.2
> Reporter: Matias Bjørling
> Priority: Trivial
> Fix For: 0.3
>
> Original Estimate: 30h
> Remaining Estimate: 30h
>
> Implement the Clustering algorithm described in "A Framework for Clustering
> Massive-Domain Data Streams" by Chary C. Aggarwal.
> Steps:
> 1. Implement baseline solution to compare solutions.
> 2. Figure out how to implement the loading of clustering by looking at the
> k-means implementation.
> 3. Implement Count-Min sketch algorithm for each cluster.
> 4. Find out how to give the user the power to choose the distance function
> for the input data ( Maybe already possible? )
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