If you're thinking along these lines, have a look at the DecisionTree implementation in MLlib. It uses the same idea and is optimized to prevent multiple passes over the data by computing several splits at each level of tree building. The tradeoff is increased model state and computation per pass over the data, but fewer total passes and hopefully lower communication overheads than, say, shuffling data around that belongs to one cluster or another. Something like that could work here as well.
I'm not super-familiar with hierarchical K-Means so perhaps there's a more efficient way to implement it, though. On Tue, Jul 8, 2014 at 2:06 PM, Hector Yee <hector....@gmail.com> wrote: > No was thinking more top-down: > > assuming a distributed kmeans system already existing, recursively apply > the kmeans algorithm on data already partitioned by the previous level of > kmeans. > > I haven't been much of a fan of bottom up approaches like HAC mainly > because they assume there is already a distance metric for items to items. > This makes it hard to cluster new content. The distances between sibling > clusters is also hard to compute (if you have thrown away the similarity > matrix), do you count paths to same parent node if you are computing > distances between items in two adjacent nodes for example. It is also a bit > harder to distribute the computation for bottom up approaches as you have > to already find the nearest neighbor to an item to begin the process. > > > On Tue, Jul 8, 2014 at 1:59 PM, RJ Nowling <rnowl...@gmail.com> wrote: > > > The scikit-learn implementation may be of interest: > > > > > > > http://scikit-learn.org/stable/modules/generated/sklearn.cluster.Ward.html#sklearn.cluster.Ward > > > > It's a bottom up approach. The pair of clusters for merging are > > chosen to minimize variance. > > > > Their code is under a BSD license so it can be used as a template. > > > > Is something like that you were thinking Hector? > > > > On Tue, Jul 8, 2014 at 4:50 PM, Dmitriy Lyubimov <dlie...@gmail.com> > > wrote: > > > sure. more interesting problem here is choosing k at each level. Kernel > > > methods seem to be most promising. > > > > > > > > > On Tue, Jul 8, 2014 at 1:31 PM, Hector Yee <hector....@gmail.com> > wrote: > > > > > >> No idea, never looked it up. Always just implemented it as doing > k-means > > >> again on each cluster. > > >> > > >> FWIW standard k-means with euclidean distance has problems too with > some > > >> dimensionality reduction methods. Swapping out the distance metric > with > > >> negative dot or cosine may help. > > >> > > >> Other more useful clustering would be hierarchical SVD. The reason > why I > > >> like hierarchical clustering is it makes for faster inference > especially > > >> over billions of users. > > >> > > >> > > >> On Tue, Jul 8, 2014 at 1:24 PM, Dmitriy Lyubimov <dlie...@gmail.com> > > >> wrote: > > >> > > >> > Hector, could you share the references for hierarchical K-means? > > thanks. > > >> > > > >> > > > >> > On Tue, Jul 8, 2014 at 1:01 PM, Hector Yee <hector....@gmail.com> > > wrote: > > >> > > > >> > > I would say for bigdata applications the most useful would be > > >> > hierarchical > > >> > > k-means with back tracking and the ability to support k nearest > > >> > centroids. > > >> > > > > >> > > > > >> > > On Tue, Jul 8, 2014 at 10:54 AM, RJ Nowling <rnowl...@gmail.com> > > >> wrote: > > >> > > > > >> > > > Hi all, > > >> > > > > > >> > > > MLlib currently has one clustering algorithm implementation, > > KMeans. > > >> > > > It would benefit from having implementations of other clustering > > >> > > > algorithms such as MiniBatch KMeans, Fuzzy C-Means, Hierarchical > > >> > > > Clustering, and Affinity Propagation. > > >> > > > > > >> > > > I recently submitted a PR [1] for a MiniBatch KMeans > > implementation, > > >> > > > and I saw an email on this list about interest in implementing > > Fuzzy > > >> > > > C-Means. > > >> > > > > > >> > > > Based on Sean Owen's review of my MiniBatch KMeans code, it > became > > >> > > > apparent that before I implement more clustering algorithms, it > > would > > >> > > > be useful to hammer out a framework to reduce code duplication > and > > >> > > > implement a consistent API. > > >> > > > > > >> > > > I'd like to gauge the interest and goals of the MLlib community: > > >> > > > > > >> > > > 1. Are you interested in having more clustering algorithms > > available? > > >> > > > > > >> > > > 2. Is the community interested in specifying a common framework? > > >> > > > > > >> > > > Thanks! > > >> > > > RJ > > >> > > > > > >> > > > [1] - https://github.com/apache/spark/pull/1248 > > >> > > > > > >> > > > > > >> > > > -- > > >> > > > em rnowl...@gmail.com > > >> > > > c 954.496.2314 > > >> > > > > > >> > > > > >> > > > > >> > > > > >> > > -- > > >> > > Yee Yang Li Hector <http://google.com/+HectorYee> > > >> > > *google.com/+HectorYee <http://google.com/+HectorYee>* > > >> > > > > >> > > > >> > > >> > > >> > > >> -- > > >> Yee Yang Li Hector <http://google.com/+HectorYee> > > >> *google.com/+HectorYee <http://google.com/+HectorYee>* > > >> > > > > > > > > -- > > em rnowl...@gmail.com > > c 954.496.2314 > > > > > > -- > Yee Yang Li Hector <http://google.com/+HectorYee> > *google.com/+HectorYee <http://google.com/+HectorYee>* >
