graphlab ted dunning clustering

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Large-scale Single-pass k-Means Clustering at ScaleTed Dunning


Large-scale Single-pass k-Means Clustering


Large-scale k-Means Clustering


Goals

Cluster very large data sets Facilitate large nearest neighbor search Allow very large number of clusters Achieve good quality– low average distance to nearest centroid on held-out data

Based on Mahout Math Runs on Hadoop (really MapR) cluster FAST – cluster tens of millions in minutes


Non-goals

Use map-reduce (but it is there) Minimize the number of clusters Support metrics other than L2


Anti-goals

Multiple passes over original data Scale as O(k n)


Why?


K-nearest Neighbor withSuper Fast k-means


What’s that?

Find the k nearest training examples Use the average value of the target variable from them

This is easy … but hard– easy because it is so conceptually simple and you don’t have knobs to turn

or models to build– hard because of the stunning amount of math– also hard because we need top 50,000 results

Initial prototype was massively too slow– 3K queries x 200K examples takes hours– needed 20M x 25M in the same time


How We Did It

2 week hackathon with 6 developers from customer bank Agile-ish development To avoid IP issues– all code is Apache Licensed (no ownership question)– all data is synthetic (no question of private data)– all development done on individual machines, hosting on Github– open is easier than closed (in this case)

Goal is new open technology to facilitate new closed solutions

Ambitious goal of ~ 1,000,000 x speedup


How We Did It

2 week hackathon with 6 developers from customer bank Agile-ish development To avoid IP issues– all code is Apache Licensed (no ownership question)– all data is synthetic (no question of private data)– all development done on individual machines, hosting on Github– open is easier than closed (in this case)

Goal is new open technology to facilitate new closed solutions

Ambitious goal of ~ 1,000,000 x speedup– well, really only 100-1000x after basic hygiene


What We Did

Mechanism for extending Mahout Vectors– DelegatingVector, WeightedVector, Centroid

Shared memory matrix– FileBasedMatrix uses mmap to share very large dense matrices

Searcher interface– ProjectionSearch, KmeansSearch, LshSearch, Brute

Super-fast clustering– Kmeans, StreamingKmeans


Projection Search

java.lang.TreeSet!


How Many Projections?


K-means Search

Simple Idea– pre-cluster the data– to find the nearest points, search the nearest clusters

Recursive application– to search a cluster, use a Searcher!


x


But This Requires k-means!

Need a new k-means algorithm to get speed– Hadoop is very slow at iterative map-reduce– Maybe Pregel clones like Giraph would be better– Or maybe not

Streaming k-means is– One pass (through the original data)– Very fast (20 us per data point with threads)– Very parallelizable


Basic Method

Use a single pass of k-means with very many clusters– output is a bad-ish clustering but a good surrogate

Use weighted centroids from step 1 to do in-memory clustering– output is a good clustering with fewer clusters


Algorithmic Details

Foreach data point xn

compute distance to nearest centroid, ∂sample u, if u > ∂/ß add to nearest centroidelse create new centroid

if number of centroids > 10 log nrecursively cluster centroidsset ß = 1.5 ß if number of centroids did not decrease


How It Works

Result is large set of centroids– these provide approximation of original distribution– we can cluster centroids to get a close approximation of clustering original– or we can just use the result directly


Parallel Speedup?

✓


Warning, Recursive Descent

Inner loop requires finding nearest centroid

With lots of centroids, this is slow

But wait, we have classes to accelerate that!






(Let’s not use k-means searcher, though)






(Let’s not use k-means searcher, though)

Empirically, projection search beats 64 bit LSH by a bit


Moving to Scale

Map-reduce implementation nearly trivial

Map: rough-cluster input data, output ß, weighted centroids

Reduce: – single reducer gets all centroids– if too many centroids, merge using recursive clustering– optionally do final clustering in-memory

Combiner possible, but essentially never important


Contact:– [email protected]– @ted_dunning

Slides and such:– http://info.mapr.com/ted-mlconf Hash tags: #mlconf #mahout #mapr

mailto:[email protected]

http://info.mapr.com/ted-mlconf

graphlab ted dunning clustering

Technology

nearest centroid

cluster centroids

new centroid

number of centroids

nearest points

large nearest neighbor

large data sets

original data scale