Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
The new k-windows algorithm for improving the k-means clustering algorithm
Journal of Complexity
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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Extracting useful knowledge from numerous distributed data repositories can be a very hard task when such data cannot be directly centralized or unified as a single file or database. This paper suggests practical distributed clustering algorithms without accessing the raw data to overcome the inefficiency of centralized data clustering methods. The aim of this research is to generate unit volume based probabilistic mixture model from local clustering results without moving original data. It has been shown that our method is appropriate for distributed clustering when real data cannot be accessed or centralized.