An Analysis of Distributed Fuzzy K-Prototype Clustering Techniques over Peer to Peer Network Environment

The challenging problems in recent years is the Distributed Data clustering over Peer-to-Peer network (P2P). In this paper, there is a presentation of peer-to-peer based K-prototype framework named MEKPFCM, by incorporating kernel fuzzy c-means and possibilistic fuzzy clustering algorithms. This work also considers the distributed clustering problem where the data and computing resources are spread over the large Peer-to-Peer network. It offers two algorithms which produce an approximate result produced by the MEKPFCM clustering algorithm. In the dynamic P2P network, the distributed fuzzy k-prototype clustering algorithm is played a major role. This can produce clustering's by local synchronization only. The second algorithm uses sampled peers that are reliable and present analytical guarantees regarding the accuracy of clustering on a P2P network. The performance of the proposed method evaluation is done through accuracy and computation time and the experimentation shows that the proposed method gives good performance compared to the existing method.