CloudDALVQ 1.0 released

CloudDALVQ is a scientific project for testing new large scale clustering/quantization algorithms. These algorithms are typically distributed and asynchronous. The code is written in C#/.NET (4.0) and runs on the Microsoft Azure Platform. The project is built using Lokad.Cloud which provides an O/C (Object to Cloud) Mapper and an 'execution framework' that leverages low level technicalities of MS Azure.

This project follows theoretical work done by Patra [1]. This project is also naturally linked to the first work of Durut and Rossi on Azure implementation of the popular batch KMeans algorithm http://apiacoa.org/publications/2010/azure-k-means.pdf


References

  1. BenoƮt Patra, "Convergence of distributed asynchronous learning vector quantization algorithms", Journal of Machine Learning Research, pp. 3431-3466, 2011.