Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Additive models, boosting, and inference for generalized divergences
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Clustering with Bregman Divergences
The Journal of Machine Learning Research
IEEE Transactions on Information Theory
On the optimality of conditional expectation as a Bregman predictor
IEEE Transactions on Information Theory
Importance partitioning in micro-aggregation
Computational Statistics & Data Analysis
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A method of fixed cardinality partition is examined. This methodology can be applied on many problems, such as the confidentiality protection, in which the protection of confidential information has to be ensured, while preserving the information content of the data. The basic feature of the technique is to aggregate the data into m groups of small fixed size k, by minimizing Bregman divergences. It is shown that, in the case of non-uniform probability measures the groups of the optimal solution are not necessarily separated by hyperplanes, while with uniform they are. After the creation of an initial partition on a real data-set, an algorithm, based on two different Bregman divergences, is proposed and applied. This methodology provides us with a very fast and efficient tool to construct a near-optimum partition for the (mxk)-partitioning problem.