GTM: the generative topographic mapping
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing Global Manifold Based on Distributed Local Data Abstractions
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning global models based on distributed data abstractions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The importance of tracing data through the visualization pipeline
Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization
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With the next-generation Web aiming to further facilitate data/information sharing and aggregation, providing data privacy protection support in an open networked environments becomes increasingly important. Learning-fromabstraction is a recently proposed distributed data mining approach which first abstracts data at local sources using the agglomerative hierarchical clustering (AGH) algorithm and then aggregates the abstractions (instead of the data) for global analysis. In this paper, we explain the limitation of the use of AGH for local manifold preserving data abstraction and propose the use of the graph-based clustering approach (e.g., the minimum cut) for local data abstraction. The effectiveness of the proposed abstraction approach was evaluated using benchmarking datasets with promising results. The global analysis results obtained based on the minimum cut abstraction was found to outperform those based on the AGH abstraction, especially when the underlying manifold was complex.