Nowadays applications produce infinite streams of data distributed across wide sensor networks. In this work we study the problem of continuously maintain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose DGClust,...

In this paper we address confidentiality issues in distributed data clustering, particularly the inference problem. We present a measure of inference risk as a function of reconstruction precision and number of colluders in a distributed data mining group. We also present KDEC-S, which is a distributed clustering algorithm designed to provide mining results while preserving confidentiality of o...

Network clustering is an important technique used in many large-scale distributed systems. Given good design and implementation, network clustering can significantly enhance the system's scalability and efficiency. However, it is very challenging to design a good clustering protocol for networks that scale fast and change continuously. In this paper, we propose a distributed network clustering ...

We study a discrete-time resource flow in Zd, where wealthier vertices attract the resources of their less rich neighbors. For any translation-invariant probability distribution of initial resource quantities, we prove that the flow at each vertex terminates after finitely many steps. This answers (a generalized version of) a question posed by Van den Berg and Meester in 1991. The proof uses th...

Distributed processing today is a largely advantageous technology of bridging together a system of multiple computers and processor systems in running applications. The concept of Distributed processing has allowed time cutting and therefore reduction in costs. Using this, we aim to address clustering techniques in developing new method for further reduction in time and costs. The problem of cl...

Clustering has become an increasingly important task in modern application domains such as marketing and purchasing assistance, multimedia, molecular biology as well as many others. In most of these areas, the data are originally collected at different sites. In order to extract information from these data, they are merged at a central site and then clustered. In this paper, we propose a differ...

In this paper we address confidentiality issues in distributed data clustering, particularly the inference problem. We present KDEC-S algorithm for distributed data clustering, which is shown to provide mining results while preserving confidentiality of original data. We also present a confidentiality framework with which we can state the confidentiality level of KDEC-S. The underlying idea of ...

One important tool is the optimal clustering of data into useful categories. Dividing similar objects into a smaller number of clusters is of importance in many applications. These include search engines, monitoring of academic performance, biology and wireless networks. We first discuss a number of clustering methods. We present a parallel algorithm for the efficient clustering of objects into...

In distributed data mining models, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a hierarchically-distributed peer-to-peer architecture and algorithm for data clustering (HP2PC). The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives...

Several algorithms have been recently developed for distributed data clustering, which are applied when data cannot be concentrated on a single machine, for instance because of privacy reasons or due to network bandwidth limitations, or because of the huge amount of distributed data. Deployed and research Peer-to-Peer systems have proven to be able to manage very large databases made up by thou...