Peer sampling gossip-based distributed clustering algorithm for unstructured P2P networks

Clustering, as an unsupervised learning method and an important process in data mining, is an aspect of large and distributed data analysis. In many applications, such as peer-to-peer systems, huge volumes of data are distributed between multiple sources. Analysis of these volumes of data and identifying appropriate clusters is challenging due to transmission, processing and storage costs. In this paper, a gossip-based distributed clustering algorithm for P2P networks called Efficient GBDC-P2P is proposed, based on an improved gossip communicative approach by combining the peer sampeling and CYCLON protocol and the idea of partitioning-based data clustering. This algorithm is appropriate for data clustering in unstructured P2P networks, and it is adapted to the dynamic conditions of these networks. In the Efficient GBDC-P2P algorithm, distributed peers perform clustering operation in a distributed way only through local communications with their neighbors. Our approach does not rely on the central server to carry out data clustering task and without the need to synchronize operations. Evaluation results verify the efficiency of our proposed algorithm for data clustering in unstructured P2P networks. Furthermore, comparative analyses with other well-established distributed clustering approaches demonstrate the superior accuracy of the proposed method.