[
https://issues.apache.org/jira/browse/MAHOUT-19?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Karl Wettin updated MAHOUT-19:
------------------------------
Attachment: MAHOUT-19.txt
This works now. Just needs a bit better tests and the tree->dot graphviz code
needs to be fixed again before I want to commit anything. Also want to try out
those training optimization strategies I've written about earlier (find closest
cluster or leaf node first and then the closest instance) and have a few small
todos.
It uses my quick and dirty PersistentMap, a Map<Writable, Writable> that keeps
all data on disk at all time using RandomAccessFile (local storage, not dfs)
but will probably be replaced by BSDed jdbm.sourceforge.net that Andrzej
pointed out.
> Hierarchial clusterer
> ---------------------
>
> Key: MAHOUT-19
> URL: https://issues.apache.org/jira/browse/MAHOUT-19
> Project: Mahout
> Issue Type: New Feature
> Components: Clustering
> Reporter: Karl Wettin
> Assignee: Karl Wettin
> Priority: Minor
> Attachments: MAHOUT-19.txt, MAHOUT-19.txt, MAHOUT-19.txt,
> TestBottomFeed.test.png, TestTopFeed.test.png
>
>
> In a hierarchial clusterer the instances are the leaf nodes in a tree where
> branch nodes contains the mean features of and the distance between its
> children.
> For performance reasons I always trained trees from the top->down. I have
> been told that it can cause various effects I never encountered. And I
> believe Huffman solved his problem by training bottom->up? The thing is, I
> don't think it is possible to train the tree top->down using map reduce. I do
> however think it is possible to train it bottom->up. I would very much
> appreciate any thoughts on this.
> Once this tree is trained one can extract clusters in various ways. The mean
> distance between all instances is usually a good maximum distance to allow
> between nodes when navigating the tree in search for a cluster.
> Navigating the tree and gather nodes that are not too far away from each
> other is usually instant if the tree is available in memory or persisted in a
> smart way. In my experience there is not much to win from extracting all
> clusters from start. Also, it usually makes sense to allow for the user to
> modify the cluster boundary variables in real time using a slider or perhaps
> present the named summary of neighbouring clusters, blacklist paths in the
> tree, etc. It is also not to bad to use secondary classification on the
> instances to create worm holes in the tree. I always thought it would be cool
> to visualize it using Touchgraph.
> My focus is on clustering text documents for instant "more like this"-feature
> in search engines and use Tanimoto similarity on the vector spaces to
> calculate the distance.
> See LUCENE-1025 for a single threaded all in memory proof of concept of a
> hierarchial clusterer.
--
This message is automatically generated by JIRA.
-
You can reply to this email to add a comment to the issue online.
