A distributed shared nearest neighbors clustering algorithm

Current data processing tasks require efficient approaches capable of dealing with large databases. A promising strategy consists in distributing the data along several computers that partially solves the undertaken problem. Then, these partial answers are integrated in order to obtain a final solution. We introduce the Distributed Shared Nearest Neighbor based clustering algorithm (D-SNN) which is able to work with disjoint partitions of data producing a global clustering solution that achieves a competitive performance regarding centralized approaches. Our algorithm is suited for large scale problems (e.g, text clustering) where data cannot be handled by a single machine due to memory size constraints. Experimental results over five data sets show that our proposal is competitive in terms of standard clustering quality performance measures.