Models of distributed data clustering in peer-to-peer environments

Distributed data mining applies techniques to mine distributed data sources by avoiding the need to first collect the data into a central site. This has a significant appeal when issues of communication cost and privacy put a restriction on traditional centralized methods. Although there has been development on many fronts in distributed data mining, we are still lacking models that abstract the process by showing similarities and contrasts between the different methods. In this paper, we introduce two abstract models for distributed clustering in peer-to-peer environments with different goals. The first is the Locally optimized Distributed Clustering (LDC) model, which aims toward achieving better local clusters at each node, and is facilitated by collaboration through sharing of summarized cluster information. The second is the Globally optimized Distributed Clustering (GDC) model, which aims toward achieving one global clustering solution that is an approximation of centralized clustering. We also report on concrete realizations of the two models that show their benefits, through application in text mining. The LDC model is realized through the Collaborative P2P Clustering algorithm, while the GDC model is realized through the Hierarchically distributed P2P Clustering algorithm. In the former, we show that peer collaboration results in significant increase in local clustering quality. The process utilizes cluster summarization to exchange information between peers. In the latter, we target scalability by structuring the P2P network hierarchically and devise a distributed variant of the k-means algorithm to compute one set of clusters across the hierarchy. We demonstrate through experimental results the effectiveness of both methods and make recommendation on when to use each method.