Density-Based Clustering for Similarity Search in a P2P Network

P2P systems represent a large portion of the Internet traffic which makes the data discovery of great importance to the user and the broad Internet community. Hence, the power of a P2P system comes from its ability to provide an efficient search service. In this paper we address the problem of similarity search in a Hybrid Overlay P2P Network which organizes data and peers in a high dimensional feature space. Data and peers are described by a set of features and clustered using a density-based algorithm. We experimentally evaluate the effectiveness of the similarity-search using uniform and zipf data distribution.