Download zip Select Archive Format
Name Last Update history
File dir clustering_test Loading commit data...
File txt .gitignore Loading commit data...
File txt algorithm_details.rst Loading commit data...
File txt readme.rst Loading commit data...
File txt reports.rst Loading commit data...

readme.rst

GSoC 2014 proposal: implementing clustering algorithms in MADlib

Synopsis

This project aims to implement some clustering algorithms in MADlib, which is a data analytics and machine learning library for PostgreSQL, Greenplum and HAWQ.

Benefits to the PostgreSQL community

Currently, only the k-means clustering algorithm is implemented in MADlib (see the doc: http://doc.madlib.net/latest/group__grp__clustering.html ). The k-medoids algorithm, while being computationnally more intensive, is much less sensitive to outliers (points that don't belong obviously to one cluster or another). This is interesting on noisy datasets, that's why I'm planning to implement it during the first part of the GSoC.

Still, these algorithms are based on distance computation, therefore they can only find convex clusters. That's why I'm proposing to implement the OPTICS (ordering points to identify the clustering structure, see http://en.wikipedia.org/wiki/OPTICS_algorithm ), which addresses this issue, as the second part of this GSoC project.

The PostgreSQL community would benefit from these features, as it would make available clustering algorithms more powerful than simple k-means.

Project details

k-medoids

The first goal of this project is to implement the k-medoids clustering algorithm. For this, I'll first spend some time studying the k-means algorithm, as both will probably be pretty similar. This will also allow me to get familiar with the codebase, the conventions, the data structures I'll need, etc.

Then I'll implement, test and debug the algorithm. If relevant, I'll also provide a "k-medoids++" version, which, similarly to the k-means++ function in MADlib, will chose the initial centroids depending on the dataset, instead of chosing them randomly. This allows to detect small clusters located far from the others (which are usually detected as part of an other bigger cluster using the standard algorithm).

The final step would be to refactor the code from k-means and k-medoids to remove any code duplication introduced in this first part.

OPTICS

The second part of this project would be to implement the density-based clustering algorithm OPTICS, which would overcome the main problem of both the k-means and k-medoids algorithm: non-convex clusters. This algorithm has been preferred over DBSCAN (http://en.wikipedia.org/wiki/DBSCAN ) as it is able to detect clusters of different densities, and, consequently, overlapping clusters.

I'll first take some time to understand full well the algorithm, and make a prototype in Python, to be sure I know how it works. Then I'll actually implement it, test it, and debug it in MADlib.

If, after that, any time's left, I'll consider implementing some of the improvements of k-means and k-medoids that we can find in the litterature.

Deliverables

  • the k-medoids algorithm in MADlib;
  • the OPTICS algorithm, also in MADlib;
  • optionnally, some improvements on k-means and/or k-medoids.

Project Schedule

  1. Implementation of the k-medoids algorithm: from 19/05 to 30/05
  2. Tests, debug and doc of k-medoids: from 31/05 to 13/06
  3. Prototype of OPTICS in Python: from 14/06 to 18/06
  4. Actual implementation of OPTICS in MADlib: from 19/06 to 25/07
  5. Tests, debug and doc of OPTICS: from 25/07 to 11/08