Learning in graphical models
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Visualizing Global Manifold Based on Distributed Local Data Abstractions
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Web intelligence (WI): what makes wisdom web?
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning global models based on distributed data abstractions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Grid-enabling data mining applications with DataMiningGrid: An architectural perspective
Future Generation Computer Systems
A service-oriented distributed data mining prototype based on JDM
Proceedings of the 2008 Spring simulation multiconference
An Interdisciplinary Perspective on IT Services Management and Service Science
Journal of Management Information Systems
A virtual mart for knowledge discovery in databases
Information Systems Frontiers
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Data mining research currently faces two great challenges: how to embrace data mining services with just-in-time and autonomous properties and how to mine distributed and privacy-protected data. To address these problems, the authors adopt the Business Process Execution Language for Web Services in a service oriented distributed data mining (DDM) platform to choreograph DDM component services and fulfill global data mining requirements. They also use the learning-from-abstraction methodology to achieve privacy-preserving DDM. Finally,they illustrate how localized autonomy on privacy-policy enforcement plusa bidding process can help the service-oriented system self-organize.