Energy efficiency, low latency, high estimation accuracy, and fast convergence are important goals in distributed incremental estimation algorithms for sensor networks. One approach that adds flexibility in achieving these goals is clustering. In this paper, the framework of distributed incremental estimation is extended by allowing clustering amongst the nodes. Among the observations made is t...

Debugging distributed applications is very di cult, due to a number of problems. To manage the inherent complexity of distributed applications, the use of abstractions is proposed. One frequently performed abstraction is to group processes into clusters. We describe an approach to derive clustering rules from well{known programming paradigms for distributed programming. Programming paradigms de...

Huge amounts of data are stored in autonomous, geographically distributed sources. The discovery of previously unknown, implicit and valuable knowledge is a key aspect of the exploitation of such sources. In recent years several approaches to knowledge discovery and data mining, and in particular to clustering, have been developed, but only a few of them are designed for distributed data source...

Most distributed classification approaches view data distribution as a technical issue and combine local models aiming at a single global model. This however, is unsuitable for inherently distributed databases, which are often described by more than one classification models that might differ conceptually. In this paper we present an approach for clustering distributed classifiers in order to d...

Clustering algorithms have emerged as a powerful learning tool to accurately analyze the massive amount of data generated by current applications and smart technologies. Precisely, their main objective is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a wide and diverse body of knowledge in the are...

We propose a distributed, light weight, scalable clustering algorithm for clustering in wireless sensor networks (WSN). The clustering algorithm is suitable in an environment where sensors are deployed randomly. The clusters are distributed over the deployment and are not necessarily of the same size in terms of the number of nodes in a cluster. The actual sizes are governed by the radius of th...

During the software lifecycle, the software structure is subject to many changes in order to fulfill the customer’s requirements. In Distributed Object Oriented systems, software engineers face many challenges to solve the software-hardware mismatch problem in which the software structure does not match the customer’s underlying hardware. A major design problem of Object Oriented software syste...

This paper introduces an approach called Clustering and Co-evolution to Construct Neural Network Ensembles (CONE). This approach creates neural network ensembles in an innovative way, by explicitly partitioning the input space through a clustering method. The clustering method allows a reduction in the number of nodes of the neural networks that compose the ensemble, thus reducing the execution...