Collaborative Document Clustering

Document clustering has been traditionally studied as a centralized process. There are scenarios when centralized clustering does not serve the required purpose; e.g. documents spanning multiple digital libraries need not be clustered in one location, but rather clustered at each location, then enriched by receiving more information from other locations. A distributed collaborative approach for document clustering is proposed in this paper. The main objective here is to allow peers in a network to form independent opinions of local document grouping, followed by exchange of cluster summaries in the form of keyphrase vectors. The nodes then expand and enrich their local solution by receiving recommended documents from their peers based on the peer judgement of the similarity of local documents to the exchanged cluster summaries. Results show improvement in final clustering after merging peer recommendations. The approach allows independent nodes to achieve better local clustering by having access to distributed data without the cost of centralized clustering, while maintaining the initial local clustering structure and coherency.