Multi-source RSC Model for Multiple Search Result Clustering
Amel Hamzaoui, Alexis Joly, Nozha Boujemaa · 2009
Heterogeneous multiple search results need to be combined and structured efficiently and generically. In this paper, we propose a new multiple search results clustering algorithm based on the relevant set correlation model. As the model is based on shared neighborhood information only, it allows our new technique to process the different information sources as simple oracles returning ranked lists if relevant objects. We experimented the proposed clustering framework and we show that our new multi-source scheme performs better than using each source independently and that it is robust to the presence of noisy oracles.