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An Enhanced k-Means Clustering Algorithm for Pattern Discovery in Healthcare Data
The huge amounts of data generated by media sensors in health monitoring systems, by
medical diagnosis that produce media (audio, video, image, and text) content, and from …
medical diagnosis that produce media (audio, video, image, and text) content, and from …
Distributed data mining
Advances in computing and communication over wired and wireless networks have resulted
in many pervasive distributed computing environments. The Internet, intranets, local area …
in many pervasive distributed computing environments. The Internet, intranets, local area …
StreetSmart traffic: Discovering and disseminating automobile congestion using VANET's
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 …
amounts of time and resources traveling through traffic congestion. Drivers choose the route …
DBDC: Density based distributed clustering
Clustering has become an increasingly important task in modern application domains such
as marketing and purchasing assistance, multimedia, molecular biology as well as many …
as marketing and purchasing assistance, multimedia, molecular biology as well as many …
Clustering distributed data streams in peer-to-peer environments
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 …
peer environment like sensor networks. The proposed technique is based on the principles …
Distributed data mining and agents
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 …
data mining (DDM) algorithms focus on one class of such distributed problem solving tasks …
Fast and exact out-of-core and distributed k-means clustering
Clustering has been one of the most widely studied topics in data mining and k-means
clustering has been one of the popular clustering algorithms. K-means requires several …
clustering has been one of the popular clustering algorithms. K-means requires several …
Collaborative fuzzy clustering algorithms: Some refinements and design guidelines
There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support
clustering data distributed across different sites. Those methods have been studied under …
clustering data distributed across different sites. Those methods have been studied under …
Hierarchically distributed peer-to-peer document clustering and cluster summarization
In distributed data mining, adopting a flat node distribution model can affect scalability. To
address the problem of modularity, flexibility and scalability, we propose a Hierarchically …
address the problem of modularity, flexibility and scalability, we propose a Hierarchically …
Distributed data mining
The continuous developments in information and communication technology have recently
led to the appearance of distributed computing environments, which comprise several, and …
led to the appearance of distributed computing environments, which comprise several, and …