Decentralized Probabilistic Text Clustering by using Distributed Hierarchical peer to peer Clustering
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2014, Vol 3, Issue 11
Abstract
Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering. For text clustering we are using decentralized probabilistic text clustering Algorithm to mine the data, it is an traditional centralized approach by using this approach analyzing massive distributed data. But it is extremely difficult to draw conclusions based on the collective characteristics of disparate data. The goal is to achieve modularity and scalability. Decentralized probabilistic text clustering Algorithm is less scalable in distributed clustering. Distributed Hierarchically Peer-to-Peer Clustering (DHP2PC) Algorithm is scalable and efficient algorithm. In this a subset of the document collection is centrally partitioned into clusters, for which cluster signatures are created. The DHP2PC algorithm finds its roots in a parallel implementation. By using cluster signatures we can mine the Massive distributed data. The algorithm offers probabilistic guarantees for the correctness of each document assignment to a cluster.
Authors and Affiliations
Attuluri Uday Kiran, Rakesh Nayak
Keywords
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- EP ID EP28112
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