Automobile traffic is a major problem in developed societies. We collectively waste huge
amounts of time and resources traveling through traffic congestion. Drivers choose the route …

This paper describes a technique for clustering homogeneously distributed data in a peer-to-
peer environment like sensor networks. The proposed technique is based on the principles …

The growing amount of available information and its distributed and heterogeneous nature
has a major impact on the field of data mining. In this paper, we propose a framework for …

Multi-agent systems (MAS) offer an architecture for distributed problem solving. Distributed
data mining (DDM) algorithms focus on one class of such distributed problem solving tasks …

We present a collective approach to learning a Bayesian network from distributed
heterogeneous data. In this approach, we first learn a local Bayesian network at each site …

Multi-agent systems offer an architecture for distributed problem solving. Distributed data
mining algorithms specialize on one class of such distributed problem solving tasks …

In this paper we present a graph-based semisupervised method for solving regression
problem. In our method, we first build an adjacent graph on all labeled and unlabeled data …

This paper documents an early effort to develop an experimental, collaborative data analysis
technique for learning classifiers from a collection of heterogeneous datasets distributed …

Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in
large data sets by partitioning the data points into similarity classes. The clustering problem …

Advances in computing and communication over wired and wireless networks have resulted
in many pervasive distributed computing environments. Many of these environments deal …