Mining massive datasets by an unsupervised parallel clustering on a GRID: Novel algorithms and case study
This paper proposes three novel parallel clustering algorithms based on the Kohonens SOM aiming at preserving the topology of the original dataset for a meaningful visualization of the results and for discovering associations between features of the dataset by topological operations over the clusters. In all these algorithms the data to be clustered are subdivided among the nodes of a GRID. In the first two algorithms each node executes an on-line SOM, whereas in the third algorithm the nodes execute a quasi-batch SOM called MANTRA. The algorithms differ on how the weights computed by the slave nodes are recombined by a master to launch the next epoch of the SOM in the nodes. A proof outline demonstrates the convergence of the proposed parallel SOMs and provides indications on how to select the learning rate to outperform both the sequential SOM and the parallel SOMs available in the literature. A case study dealing with bioinformatics is presented to illustrate that by our parallel SOM we may obtain meaningful clusters in massive data mining applications at a fraction of the time needed by the sequential SOM, and that the obtained classification supports a fruitful knowledge extraction from massive datasets.
clustering algorithm bioinformatics neural networks grid computing parallel algorithms